diff --git a/backend/api/services/citation_export_service.py b/backend/api/services/citation_export_service.py
index a59534cb..05ecccb6 100644
--- a/backend/api/services/citation_export_service.py
+++ b/backend/api/services/citation_export_service.py
@@ -13,7 +13,9 @@
from ..citations.export_models import ExportDimension
from ..citations.export_models import ExportGroup
from ..citations.export_models import ExportItem
-from .cit_db_service import cits_dp_service, snake_case, snake_case_param
+from .cit_db_service import cits_dp_service
+from .cit_db_service import snake_case
+from .cit_db_service import snake_case_param
class ExportValidationError(ValueError):
@@ -35,7 +37,8 @@ class ExportValidationError(ValueError):
('citation.journal', 'Journal', ('journal', 'secondary_title'), 'Journal', True),
('citation.year', 'Year', ('year', 'publication_year'), 'Year', True),
('citation.doi', 'DOI', ('doi',), 'DOI', True),
- ('citation.type', 'Reference type', ('type', 'type_of_reference'), 'Reference type', True),
+ ('citation.type', 'Reference type',
+ ('type', 'type_of_reference'), 'Reference type', True),
('citation.keywords', 'Keywords', ('keywords',), 'Keywords', True),
('citation.notes', 'Notes', ('notes',), 'Notes', False),
)
@@ -80,22 +83,38 @@ def _dimensions(spec) -> list[ExportDimension]:
return [ExportDimension(id=i, label=label, default_selected=default) for i, label, default in spec]
+def _screening_llm_candidates(stage: str, key: str) -> tuple[str, ...]:
+ """Return preferred-to-legacy screening AI column candidates."""
+ return (f'llm_{stage}_{key}', f'llm_{key}')
+
+
+def _screening_human_candidates(stage: str, key: str) -> tuple[str, ...]:
+ """Return preferred-to-legacy screening human column candidates."""
+ return (f'human_{stage}_{key}', f'human_{key}')
+
+
class CitationExportService:
def build_schema(
self, sr: dict[str, Any], table_name: str, columns: list[dict[str, str]] | None = None,
) -> CitationExportSchema:
- columns = columns if columns is not None else cits_dp_service.get_table_columns(table_name)
- existing = {str(c['column_name']).casefold(): str(c['column_name']) for c in columns}
+ columns = columns if columns is not None else cits_dp_service.get_table_columns(
+ table_name)
+ existing = {str(c['column_name']).casefold(): str(
+ c['column_name']) for c in columns}
citation_items: list[ExportItem] = []
for semantic_id, label, aliases, _header, default in CITATION_FIELDS:
- physical = next((existing[a.casefold()] for a in aliases if a.casefold() in existing), None)
+ physical = next((existing[a.casefold()]
+ for a in aliases if a.casefold() in existing), None)
if physical and not _denied(physical):
- citation_items.append(ExportItem(id=semantic_id, label=label, default_selected=default))
+ citation_items.append(ExportItem(
+ id=semantic_id, label=label, default_selected=default))
cp = (sr or {}).get('criteria_parsed') or {}
- l1 = self._screen_items('l1', (cp.get('l1') or {}).get('questions') or [], existing)
- l2 = self._screen_items('l2', (cp.get('l2') or {}).get('questions') or [], existing)
+ l1 = self._screen_items(
+ 'l1', (cp.get('l1') or {}).get('questions') or [], existing)
+ l2 = self._screen_items(
+ 'l2', (cp.get('l2') or {}).get('questions') or [], existing)
params = self._parameter_items(cp.get('parameters') or {}, existing)
# A shared physical key in two current stages is ambiguous and must not be guessed.
@@ -112,12 +131,18 @@ def build_schema(
)
all_keys[key] = stage
- return CitationExportSchema(groups=[
- ExportGroup(id='citation', label='Citation details', items=citation_items),
- ExportGroup(id='l1', label='L1 screening', dimensions=_dimensions(SCREEN_DIMENSIONS), items=l1),
- ExportGroup(id='l2', label='L2 screening', dimensions=_dimensions(SCREEN_DIMENSIONS), items=l2),
- ExportGroup(id='parameters', label='Extracted parameters', dimensions=_dimensions(PARAM_DIMENSIONS), items=params),
- ])
+ return CitationExportSchema(
+ groups=[
+ ExportGroup(id='citation', label='Citation details',
+ items=citation_items),
+ ExportGroup(id='l1', label='L1 screening',
+ dimensions=_dimensions(SCREEN_DIMENSIONS), items=l1),
+ ExportGroup(id='l2', label='L2 screening',
+ dimensions=_dimensions(SCREEN_DIMENSIONS), items=l2),
+ ExportGroup(id='parameters', label='Extracted parameters',
+ dimensions=_dimensions(PARAM_DIMENSIONS), items=params),
+ ],
+ )
def _screen_items(self, stage: str, questions: list[Any], existing: dict[str, str]) -> list[ExportItem]:
items: list[ExportItem] = []
@@ -127,18 +152,22 @@ def _screen_items(self, stage: str, questions: list[Any], existing: dict[str, st
continue
key = screening_key(value)
if key in seen:
- raise ExportValidationError(f'Current {stage.upper()} questions resolve to duplicate key {key!r}')
+ raise ExportValidationError(
+ f'Current {stage.upper()} questions resolve to duplicate key {key!r}')
seen.add(key)
available = []
- if f'human_{key}'.casefold() in existing:
+ if any(candidate.casefold() in existing for candidate in _screening_human_candidates(stage, key)):
available.append('human_answer')
- if f'llm_{key}'.casefold() in existing:
- available.extend(['ai_answer', 'ai_explanation', 'confidence', 'evidence'])
- items.append(ExportItem(
- id=f'{stage}.{key}', label=value,
- default_selected='human_answer' in available,
- available_dimensions=available,
- ))
+ if any(candidate.casefold() in existing for candidate in _screening_llm_candidates(stage, key)):
+ available.extend(
+ ['ai_answer', 'ai_explanation', 'confidence', 'evidence'])
+ items.append(
+ ExportItem(
+ id=f'{stage}.{key}', label=value,
+ default_selected='human_answer' in available,
+ available_dimensions=available,
+ ),
+ )
return items
def _parameter_items(self, params: dict[str, Any], existing: dict[str, str]) -> list[ExportItem]:
@@ -147,23 +176,28 @@ def _parameter_items(self, params: dict[str, Any], existing: dict[str, str]) ->
items: list[ExportItem] = []
seen: set[str] = set()
for index, names in enumerate(possible):
- category = str(categories[index]) if index < len(categories) else None
+ category = str(categories[index]) if index < len(
+ categories) else None
for value in names if isinstance(names, list) else []:
if not isinstance(value, str) or not value.strip():
continue
key = parameter_key(value)
if key in seen:
- raise ExportValidationError(f'Current parameters resolve to duplicate key {key!r}')
+ raise ExportValidationError(
+ f'Current parameters resolve to duplicate key {key!r}')
seen.add(key)
available = []
if f'human_param_{key}'.casefold() in existing:
available.append('human_value')
if f'llm_param_{key}'.casefold() in existing:
- available.extend(['ai_value', 'ai_explanation', 'evidence'])
- items.append(ExportItem(
- id=f'parameters.{key}', label=value, category=category,
- available_dimensions=available,
- ))
+ available.extend(
+ ['ai_value', 'ai_explanation', 'evidence'])
+ items.append(
+ ExportItem(
+ id=f'parameters.{key}', label=value, category=category,
+ available_dimensions=available,
+ ),
+ )
return items
def resolve(self, schema: CitationExportSchema, request: CitationExportRequest) -> list[OutputField]:
@@ -171,19 +205,24 @@ def resolve(self, schema: CitationExportSchema, request: CitationExportRequest)
selected: dict[str, tuple[set[str], set[str]]] = {}
for selection in request.selections:
if selection.group in selected:
- raise ExportValidationError(f'Duplicate selection group: {selection.group}')
+ raise ExportValidationError(
+ f'Duplicate selection group: {selection.group}')
group = groups.get(selection.group)
if not group:
- raise ExportValidationError(f'Unknown export group: {selection.group}')
+ raise ExportValidationError(
+ f'Unknown export group: {selection.group}')
valid_items = {item.id for item in group.items}
valid_dimensions = {dimension.id for dimension in group.dimensions}
unknown_items = set(selection.items) - valid_items
unknown_dimensions = set(selection.dimensions) - valid_dimensions
if unknown_items or unknown_dimensions:
- raise ExportValidationError('Export selection contains stale or unknown fields')
+ raise ExportValidationError(
+ 'Export selection contains stale or unknown fields')
if group.id != 'citation' and selection.items and not selection.dimensions:
- raise ExportValidationError(f'{group.id} requires at least one dimension')
- selected[group.id] = (set(selection.items), set(selection.dimensions))
+ raise ExportValidationError(
+ f'{group.id} requires at least one dimension')
+ selected[group.id] = (set(selection.items),
+ set(selection.dimensions))
fields: list[OutputField] = []
for group in schema.groups:
@@ -192,7 +231,8 @@ def resolve(self, schema: CitationExportSchema, request: CitationExportRequest)
if item.id not in item_ids:
continue
if group.id == 'citation':
- registry = next(row for row in CITATION_FIELDS if row[0] == item.id)
+ registry = next(
+ row for row in CITATION_FIELDS if row[0] == item.id)
# build_schema proved one alias exists; resolve again from current metadata below.
fields.append(OutputField(registry[3], registry[2][0]))
continue
@@ -200,27 +240,38 @@ def resolve(self, schema: CitationExportSchema, request: CitationExportRequest)
key = item.id.split('.', 1)[1]
applicable = dimensions & available
if not applicable:
- raise ExportValidationError(f'Requested data is unavailable for {item.id}')
+ raise ExportValidationError(
+ f'Requested data is unavailable for {item.id}')
for dimension in [d.id for d in group.dimensions if d.id in applicable]:
- fields.extend(self._dimension_fields(group.id, item.label, key, dimension))
+ fields.extend(self._dimension_fields(
+ group.id, item.label, key, dimension))
if not fields:
- raise ExportValidationError('At least one output field must be selected')
+ raise ExportValidationError(
+ 'At least one output field must be selected')
return fields
def _dimension_fields(self, group: str, label: str, key: str, dimension: str) -> list[OutputField]:
if group in ('l1', 'l2'):
source = 'human' if dimension == 'human_answer' else 'llm'
- prop = {'human_answer': 'selected', 'ai_answer': 'selected', 'ai_explanation': 'explanation',
- 'confidence': 'confidence', 'evidence': 'evidence_sentences'}[dimension]
- suffix = {'human_answer': 'Human answer', 'ai_answer': 'AI answer',
- 'ai_explanation': 'AI explanation', 'confidence': 'Confidence', 'evidence': 'Evidence'}[dimension]
- return [OutputField(f'{group.upper()} | {label} | {suffix}', f'{source}_{key}', prop)]
+ prop = {
+ 'human_answer': 'selected', 'ai_answer': 'selected', 'ai_explanation': 'explanation',
+ 'confidence': 'confidence', 'evidence': 'evidence_sentences',
+ }[dimension]
+ suffix = {
+ 'human_answer': 'Human answer', 'ai_answer': 'AI answer',
+ 'ai_explanation': 'AI explanation', 'confidence': 'Confidence', 'evidence': 'Evidence',
+ }[dimension]
+ return [OutputField(f'{group.upper()} | {label} | {suffix}', f'{source}_{group}_{key}', prop)]
source = 'human' if dimension == 'human_value' else 'llm'
column = f'{source}_param_{key}'
if dimension in ('human_value', 'ai_value'):
prefix = 'Human' if source == 'human' else 'AI'
- return [OutputField(f'Parameter | {label} | {prefix} found', column, 'found'),
- OutputField(f'Parameter | {label} | {prefix} value', column, 'value')]
+ return [
+ OutputField(
+ f'Parameter | {label} | {prefix} found', column, 'found'),
+ OutputField(
+ f'Parameter | {label} | {prefix} value', column, 'value'),
+ ]
prop = 'explanation' if dimension == 'ai_explanation' else 'evidence_sentences'
suffix = 'AI explanation' if dimension == 'ai_explanation' else 'Evidence'
return [OutputField(f'Parameter | {label} | {suffix}', column, prop)]
@@ -229,38 +280,59 @@ def export_csv(
self, table_name: str, sr: dict[str, Any], request: CitationExportRequest,
) -> bytes:
columns = cits_dp_service.get_table_columns(table_name)
- actual = {str(c['column_name']).casefold(): str(c['column_name']) for c in columns}
+ actual = {str(c['column_name']).casefold(): str(
+ c['column_name']) for c in columns}
schema = self.build_schema(sr, table_name, columns)
fields = self.resolve(schema, request)
# Resolve citation aliases and preserve only identifiers proven by current metadata.
resolved = []
for field in fields:
physical = actual.get(field.column.casefold())
+ if not physical and field.column.startswith('human_l1_'):
+ legacy = f"human_{field.column.removeprefix('human_l1_')}"
+ physical = actual.get(legacy.casefold())
+ if not physical and field.column.startswith('human_l2_'):
+ legacy = f"human_{field.column.removeprefix('human_l2_')}"
+ physical = actual.get(legacy.casefold())
+ if not physical and field.column.startswith('llm_l1_'):
+ legacy = f"llm_{field.column.removeprefix('llm_l1_')}"
+ physical = actual.get(legacy.casefold())
+ if not physical and field.column.startswith('llm_l2_'):
+ legacy = f"llm_{field.column.removeprefix('llm_l2_')}"
+ physical = actual.get(legacy.casefold())
if not physical and field.property is None:
- registry = next(row for row in CITATION_FIELDS if row[3] == field.header)
- physical = next((actual.get(alias.casefold()) for alias in registry[2] if actual.get(alias.casefold())), None)
+ registry = next(
+ row for row in CITATION_FIELDS if row[3] == field.header)
+ physical = next((actual.get(alias.casefold())
+ for alias in registry[2] if actual.get(alias.casefold())), None)
if not physical or _denied(physical):
- raise ExportValidationError(f'Export field is no longer available: {field.header}')
- resolved.append(OutputField(field.header, physical, field.property))
+ raise ExportValidationError(
+ f'Export field is no longer available: {field.header}')
+ resolved.append(OutputField(
+ field.header, physical, field.property))
scope = request.row_scope
if scope.kind in ('l1_included', 'l2_included'):
- cits_dp_service.backfill_human_decisions((sr or {}).get('criteria_parsed') or {}, table_name)
+ cits_dp_service.backfill_human_decisions(
+ (sr or {}).get('criteria_parsed') or {}, table_name)
rows = cits_dp_service.fetch_export_rows(
- table_name, sorted({field.column for field in resolved}), scope.kind,
+ table_name, sorted(
+ {field.column for field in resolved}), scope.kind,
scope.citation_ids,
)
if scope.kind == 'citation_ids':
requested_ids = set(scope.citation_ids or [])
returned_ids = {int(row['id']) for row in rows}
if requested_ids != returned_ids:
- raise ExportValidationError('One or more citation IDs do not belong to this review')
+ raise ExportValidationError(
+ 'One or more citation IDs do not belong to this review')
output = io.StringIO(newline='')
writer = csv.writer(output)
writer.writerow([field.header for field in resolved])
for row in rows:
- writer.writerow([self._cell(row.get(field.column), field.property) for field in resolved])
+ writer.writerow(
+ [self._cell(row.get(field.column), field.property) for field in resolved])
return output.getvalue().encode('utf-8-sig')
def _cell(self, value: Any, prop: str | None) -> str:
@@ -283,4 +355,4 @@ def _cell(self, value: Any, prop: str | None) -> str:
return text
-citation_export_service = CitationExportService()
\ No newline at end of file
+citation_export_service = CitationExportService()
diff --git a/backend/tests/citation_export_service_test.py b/backend/tests/citation_export_service_test.py
index f56a0dc0..f19ace52 100644
--- a/backend/tests/citation_export_service_test.py
+++ b/backend/tests/citation_export_service_test.py
@@ -32,7 +32,8 @@ def test_schema_is_current_config_allowlist_and_excludes_sensitive_columns():
schema = citation_export_service.build_schema(
_sr(), 'unused', _columns(
'id', 'title', 'fulltext_url', 'prompt',
- 'human_current_question', 'llm_current_question',
+ 'human_l1_current_question', 'llm_l1_current_question',
+ 'human_l2_current_full_text_question', 'llm_l2_current_full_text_question',
'human_stale_old_question', 'human_param_attack_rate',
),
)
@@ -158,8 +159,8 @@ def test_parameter_dimensions_expand_in_stable_order():
def test_select_all_dimensions_uses_each_items_available_intersection():
schema = citation_export_service.build_schema(
_sr(), 'unused', _columns(
- 'id', 'human_current_question',
- 'llm_current_full_text_question',
+ 'id', 'human_l1_current_question',
+ 'llm_l2_current_full_text_question',
),
)
request = CitationExportRequest(
@@ -187,7 +188,7 @@ def test_select_all_dimensions_uses_each_items_available_intersection():
def test_csv_is_ordered_normalized_and_formula_safe(monkeypatch):
- columns = _columns('id', 'title', 'human_current_question')
+ columns = _columns('id', 'title', 'human_l1_current_question')
monkeypatch.setattr(
cits_dp_service, 'get_table_columns',
lambda _table: columns,
@@ -196,7 +197,7 @@ def test_csv_is_ordered_normalized_and_formula_safe(monkeypatch):
cits_dp_service, 'fetch_export_rows',
lambda *_args, **_kwargs: [{
'id': 1, 'title': '=HYPERLINK("bad")',
- 'human_current_question': {'selected': 'Include, yes'},
+ 'human_l1_current_question': {'selected': 'Include, yes'},
}],
)
request = CitationExportRequest(
@@ -217,6 +218,70 @@ def test_csv_is_ordered_normalized_and_formula_safe(monkeypatch):
assert rows[1] == ['\'=HYPERLINK("bad")', 'Include, yes']
+def test_export_uses_stage_prefixed_screening_columns(monkeypatch):
+ columns = _columns(
+ 'human_current_full_text_question',
+ 'human_l1_current_question',
+ 'llm_l1_current_question',
+ 'human_l2_current_full_text_question',
+ 'llm_l2_current_full_text_question',
+ )
+ requested_columns = []
+ monkeypatch.setattr(
+ cits_dp_service, 'get_table_columns',
+ lambda _table: columns,
+ )
+ monkeypatch.setattr(
+ cits_dp_service, 'fetch_export_rows',
+ lambda _table, export_columns, *_args, **_kwargs: requested_columns.extend(export_columns) or [{
+ 'id': 1,
+ 'human_l1_current_question': {'selected': 'Include'},
+ 'llm_l1_current_question': {'selected': 'Include', 'confidence': 0.93},
+ 'human_current_full_text_question': {'selected': 'Exclude'},
+ 'human_l2_current_full_text_question': {'selected': 'Exclude'},
+ 'llm_l2_current_full_text_question': {
+ 'selected': 'Exclude',
+ 'confidence': 0.81,
+ 'explanation': 'Full-text rationale',
+ 'evidence_sentences': ['Sentence 1'],
+ },
+ }],
+ )
+ request = CitationExportRequest(
+ selections=[
+ {
+ 'group': 'l1',
+ 'items': ['l1.current_question'],
+ 'dimensions': ['human_answer', 'ai_answer'],
+ },
+ {
+ 'group': 'l2',
+ 'items': ['l2.current_full_text_question'],
+ 'dimensions': ['human_answer', 'ai_answer', 'confidence'],
+ },
+ ],
+ )
+
+ body = citation_export_service.export_csv(
+ 'citations', _sr(), request).decode('utf-8-sig')
+ rows = list(csv.reader(io.StringIO(body)))
+
+ assert requested_columns == [
+ 'human_current_full_text_question',
+ 'human_l2_current_full_text_question',
+ 'llm_l1_current_question',
+ 'llm_l2_current_full_text_question',
+ ]
+ assert rows[0] == [
+ 'L1 | Current question? | Human answer',
+ 'L1 | Current question? | AI answer',
+ 'L2 | Current full text question? | Human answer',
+ 'L2 | Current full text question? | AI answer',
+ 'L2 | Current full text question? | Confidence',
+ ]
+ assert rows[1] == ['Include', 'Include', 'Exclude', 'Exclude', '0.81']
+
+
def test_citation_id_scope_must_be_entirely_owned(monkeypatch):
monkeypatch.setattr(
cits_dp_service, 'get_table_columns',
diff --git a/frontend/app/[lang]/can-sr/l1-screen/view/page.tsx b/frontend/app/[lang]/can-sr/l1-screen/view/page.tsx
index 8df3f63b..8c96e94a 100644
--- a/frontend/app/[lang]/can-sr/l1-screen/view/page.tsx
+++ b/frontend/app/[lang]/can-sr/l1-screen/view/page.tsx
@@ -8,7 +8,13 @@ import { getAuthToken, getTokenType } from '@/lib/auth'
import { Wand2 } from 'lucide-react'
import { useDictionary } from '@/app/[lang]/DictionaryProvider'
import { needsHumanReviewForCriterion } from '@/components/can-sr/needsHumanReview'
-import { ScreeningCitationContext, extractHumanAnswer, humanAnswerStatus, resolveConfiguredValue, resolveDisplayedAnswer } from '@/components/can-sr/screening-citation-context'
+import {
+ ScreeningCitationContext,
+ resolveConfiguredAnswerWarning,
+ resolveDisplayedAnswer,
+ resolveScreeningAnswerValue,
+ screeningAnswerColumn,
+} from '@/components/can-sr/screening-citation-context'
/*
Title & Abstract single-citation viewer for L1 screening.
@@ -47,15 +53,6 @@ function snakeCaseColumn(name: string) {
return `llm_${s}`.slice(0, 60)
}
-/**
- * Human classification column for screening.
- * Example: question -> human_question
- */
-function humanScreenColumn(name: string) {
- const base = snakeCaseColumn(name)
- return base.replace(/^llm_/, 'human_')
-}
-
type ValidationEntry = { user: string; validated_at: string }
function parseValidations(v: any): ValidationEntry[] {
@@ -295,8 +292,8 @@ export default function CanSrL1ScreenPage() {
if (!res.ok) {
setError(
data?.error ||
- data?.detail ||
- `Failed to load citation (${res.status})`,
+ data?.detail ||
+ `Failed to load citation (${res.status})`,
)
setCitation(null)
} else {
@@ -447,9 +444,14 @@ export default function CanSrL1ScreenPage() {
criteriaData.questions.forEach((q: string, idx: number) => {
const llmCol = snakeCaseColumn(q)
- const humanCol = criteriaData.items?.[idx]?.answer_column || humanScreenColumn(q)
+ const configuredAnswerColumn = criteriaData.items?.[idx]?.answer_column
- const humanRaw = resolveConfiguredValue(citation as any, humanCol)
+ const humanRaw = resolveScreeningAnswerValue(
+ citation as any,
+ q,
+ 'l1',
+ 'human',
+ )
const llmRaw =
(citation as any)?.[`llm_l1_${llmCol.replace(/^llm_/, '')}`] ??
(citation as any)?.[llmCol]
@@ -539,6 +541,25 @@ export default function CanSrL1ScreenPage() {
if (!criteriaData) return
const question = criteriaData.questions[questionIndex]
const ok = await postHumanClassifyPayload(question, value)
+ if (ok) {
+ const configuredAnswerColumn =
+ criteriaData.items?.[questionIndex]?.answer_column
+ setCitation((prev) => {
+ if (!prev) return prev
+ const next = {
+ ...prev,
+ [screeningAnswerColumn(question, 'human', 'l1')]: value,
+ }
+ if (
+ configuredAnswerColumn &&
+ configuredAnswerColumn.trim().length > 0 &&
+ !(configuredAnswerColumn in next)
+ ) {
+ next[configuredAnswerColumn] = value
+ }
+ return next
+ })
+ }
setSaveStatus((prev) => ({ ...prev, [questionIndex]: ok ? 'saved' : 'error' }))
}
@@ -582,23 +603,78 @@ export default function CanSrL1ScreenPage() {
) throw new Error('AI response did not match the requested citation')
if (currentCitationKeyRef.current !== requestKey) return false
+ const criteriaResult = Array.isArray(runData?.criteria)
+ ? runData.criteria.find(
+ (item: any) =>
+ String(item?.criterion_key || '') === ck
+ || String(item?.question || '') === q,
+ )
+ : null
+ const immediateAiPanel = criteriaResult?.screening
+ ? {
+ selected: criteriaResult.screening.answer ?? '',
+ confidence: criteriaResult.screening.confidence,
+ explanation: criteriaResult.screening.rationale ?? '',
+ evidence_sentences: [],
+ evidence_tables: [],
+ evidence_figures: [],
+ source: 'agentic',
+ pipeline: 'title_abstract',
+ }
+ : null
+ if (immediateAiPanel) {
+ const configuredAnswerColumn = criteriaData?.items?.[questionIndex]?.answer_column
+ const displayed = resolveDisplayedAnswer(
+ resolveScreeningAnswerValue(
+ citation as any,
+ q,
+ 'l1',
+ 'human',
+ ),
+ immediateAiPanel,
+ )
+ setAiPanels((prev) => ({ ...prev, [questionIndex]: immediateAiPanel }))
+ if (displayed) {
+ setSelections((prev) => ({ ...prev, [questionIndex]: displayed }))
+ }
+ }
+
// Targeted update: fetch only the llm_* column for this criterion and update
// aiPanels directly — no full page re-render.
try {
- const llmColName = `llm_${ck}`
+ const llmColName = screeningAnswerColumn(q, 'llm', 'l1')
+ const configuredAnswerColumn = criteriaData?.items?.[questionIndex]?.answer_column
const citRes = await fetch(
`/api/can-sr/citations/get?sr_id=${encodeURIComponent(runContext.srId)}&citation_id=${encodeURIComponent(runContext.citationId)}`,
{ method: 'GET', headers: getAuthHeaders() },
)
const citData = await citRes.json().catch(() => ({}))
if (currentCitationKeyRef.current === requestKey && citRes.ok && citData) {
- const llmRaw = citData[llmColName]
+ const llmRaw =
+ resolveScreeningAnswerValue(citData, q, 'l1', 'llm', [`llm_${ck}`])
+ ?? citData[llmColName]
let llmParsed = llmRaw
if (typeof llmRaw === 'string') {
try { llmParsed = JSON.parse(llmRaw) } catch { llmParsed = llmRaw }
}
- if (llmParsed && typeof llmParsed === 'object') {
- setAiPanels((prev) => ({ ...prev, [questionIndex]: llmParsed }))
+ const refreshedAiPanel =
+ llmParsed && typeof llmParsed === 'object'
+ ? llmParsed
+ : typeof llmParsed === 'string' && llmParsed
+ ? { selected: llmParsed }
+ : null
+ if (refreshedAiPanel) {
+ setAiPanels((prev) => ({ ...prev, [questionIndex]: refreshedAiPanel }))
+ const humanRaw = resolveScreeningAnswerValue(
+ citData,
+ q,
+ 'l1',
+ 'human',
+ )
+ const displayed = resolveDisplayedAnswer(humanRaw, refreshedAiPanel)
+ if (displayed) {
+ setSelections((prev) => ({ ...prev, [questionIndex]: displayed }))
+ }
}
}
} catch { /* best-effort */ }
@@ -748,8 +824,16 @@ export default function CanSrL1ScreenPage() {
{criteriaData.questions.map((q, idx) => {
const options = criteriaData.possible_answers[idx] || []
- const answerColumn = criteriaData.items?.[idx]?.answer_column || null
- const answerStatus = humanAnswerStatus(citation!, answerColumn, options)
+ const answerColumn =
+ screeningAnswerColumn(q, 'human', 'l1')
+ const configuredAnswerColumn =
+ criteriaData.items?.[idx]?.answer_column || null
+ const answerWarning = resolveConfiguredAnswerWarning(
+ citation!,
+ answerColumn,
+ configuredAnswerColumn,
+ options,
+ )
const current = selections[idx] ?? ''
const aiData = aiPanels[idx]
// Per-question highlight aligned with list/back-end logic.
@@ -792,8 +876,8 @@ export default function CanSrL1ScreenPage() {
@@ -816,9 +900,14 @@ export default function CanSrL1ScreenPage() {
))}
- {answerStatus !== 'unconfigured' && answerStatus !== 'matched' ? (
+ {answerWarning ? (
- Human answer column “{answerColumn}”: {answerStatus === 'missing' ? 'not found in this citation' : answerStatus === 'blank' ? 'blank for this citation' : 'value does not match an available answer'}.
+ Fallback human answer source column “{answerWarning.column}”:{' '}
+ {answerWarning.status === 'missing'
+ ? 'not found in this citation'
+ : answerWarning.status === 'blank'
+ ? 'blank for this citation'
+ : 'value does not match an available answer'}.
) : null}
@@ -908,20 +997,20 @@ export default function CanSrL1ScreenPage() {
-
- {critDisagrees ? (
-
- Critical review recommends a different answer: {String((crit as any)?.answer ?? '—')}
-
- ) : (
- Critical review supports the screening answer
- )}
-
-
Judgment confidence: {String((crit as any)?.confidence ?? '—')}
-
+
+ {critDisagrees ? (
+
+ Critical review recommends a different answer: {String((crit as any)?.answer ?? '—')}
+
+ ) : (
+ Critical review supports the screening answer
+ )}
+
+
Judgment confidence: {String((crit as any)?.confidence ?? '—')}
+
- Human answer column “{answerColumn}”:{' '}
- {answerStatus === 'missing'
+ Fallback human answer source column “
+ {answerWarning.column}”:{' '}
+ {answerWarning.status === 'missing'
? 'not found in this citation'
- : answerStatus === 'blank'
+ : answerWarning.status === 'blank'
? 'blank for this citation'
: 'value does not match an available answer'}
.
diff --git a/frontend/components/can-sr/PagedList.tsx b/frontend/components/can-sr/PagedList.tsx
index ec0ea8be..f94ab5a1 100644
--- a/frontend/components/can-sr/PagedList.tsx
+++ b/frontend/components/can-sr/PagedList.tsx
@@ -6,6 +6,9 @@ import { Bot, Check } from 'lucide-react'
import { useParams, useRouter } from 'next/navigation'
import React, { ChangeEvent, useEffect, useRef, useState } from 'react'
import { SimpleTooltip} from '@/components/ui/tooltip'
+import {
+ resolveScreeningAnswerValue,
+} from '@/components/can-sr/screening-citation-context'
type CitationInfo = {
citationIds: number[]
@@ -90,6 +93,18 @@ function criterionKeyFromQuestion(question: string) {
return s.slice(0, 56)
}
+function screeningAnswerFallbackHeaders(
+ question: string,
+ step: 'l1' | 'l2',
+ prefix: 'human' | 'llm',
+) {
+ const headers: string[] = [snakeCaseColumn(question, prefix === 'llm')]
+ if (step === 'l2') {
+ headers.unshift(`${prefix}_l1_${criterionKeyFromQuestion(question)}`)
+ }
+ return headers
+}
+
export default function PagedList({
citationIds,
srId,
@@ -218,10 +233,19 @@ export default function PagedList({
let classified = true
let verified = true
for (const question of questions) {
- const llmQuestion = snakeCaseColumn(question, true)
- const humanQuestion = snakeCaseColumn(question, false)
- if (!row?.[llmQuestion]) classified = false
- if (!row?.[humanQuestion]) verified = false
+ const stage = screeningStep === 'l2' ? 'l2' : screeningStep === 'l1' ? 'l1' : null
+ const llmValue = stage
+ ? resolveScreeningAnswerValue(row, question, stage, 'llm', [
+ ...screeningAnswerFallbackHeaders(question, stage, 'llm'),
+ ])
+ : row?.[snakeCaseColumn(question, true)]
+ const humanValue = stage
+ ? resolveScreeningAnswerValue(row, question, stage, 'human', [
+ ...screeningAnswerFallbackHeaders(question, stage, 'human'),
+ ])
+ : row?.[snakeCaseColumn(question, false)]
+ if (!llmValue) classified = false
+ if (!humanValue) verified = false
}
if (classified) nextLlm[id] = true
@@ -333,8 +357,11 @@ export default function PagedList({
for (const q of questions || []) {
if (!q) continue
- const llmCol = snakeCaseColumn(q, true)
- const llmVal = row?.[llmCol]
+ const llmVal = screeningStep === 'l1' || screeningStep === 'l2'
+ ? resolveScreeningAnswerValue(row, q, screeningStep, 'llm', [
+ ...screeningAnswerFallbackHeaders(q, screeningStep, 'llm'),
+ ])
+ : row?.[snakeCaseColumn(q, true)]
if (!llmVal) continue
hasAnyLlm = true
let conf: number | null = null
@@ -427,8 +454,14 @@ export default function PagedList({
const needs = computeNeedsValidation(id, row)
const runs = latestRunsByCitation[id] || []
const notScreened = (!runs.length) && !questions.some((q) => {
- const llmCol = snakeCaseColumn(q, true)
- return Boolean(row?.[llmCol])
+ if (screeningStep === 'l1' || screeningStep === 'l2') {
+ return Boolean(
+ resolveScreeningAnswerValue(row, q, screeningStep, 'llm', [
+ ...screeningAnswerFallbackHeaders(q, screeningStep, 'llm'),
+ ]),
+ )
+ }
+ return Boolean(row?.[snakeCaseColumn(q, true)])
})
const unvalidated = !validated
if (filterMode === 'all') return true
diff --git a/frontend/components/can-sr/screening-citation-context.test.tsx b/frontend/components/can-sr/screening-citation-context.test.tsx
index 46e8326c..7966fd9d 100644
--- a/frontend/components/can-sr/screening-citation-context.test.tsx
+++ b/frontend/components/can-sr/screening-citation-context.test.tsx
@@ -2,6 +2,7 @@ import { describe, expect, it } from 'vitest'
import { render, screen } from '@testing-library/react'
import {
humanAnswerStatus,
+ resolveScreeningAnswerValue,
resolveDisplayedAnswer,
resolveConfiguredValue,
ScreeningCitationContext,
@@ -28,6 +29,36 @@ describe('screening citation context helpers', () => {
expect(resolveDisplayedAnswer('', JSON.stringify({ selected: 'Include' }))).toBe('Include')
expect(resolveDisplayedAnswer(null, null)).toBe('')
})
+
+ it('falls back to legacy human screening columns', () => {
+ expect(
+ resolveScreeningAnswerValue(
+ { human_current_question: { selected: 'Include' } },
+ 'Current question?',
+ 'l1',
+ 'human',
+ ),
+ ).toEqual({ selected: 'Include' })
+ })
+
+ it('allows l2 human answers to fall back to l1 and legacy human columns', () => {
+ expect(
+ resolveScreeningAnswerValue(
+ { human_l1_current_full_text_question: { selected: 'Exclude' } },
+ 'Current full text question?',
+ 'l2',
+ 'human',
+ ),
+ ).toEqual({ selected: 'Exclude' })
+ expect(
+ resolveScreeningAnswerValue(
+ { human_current_full_text_question: { selected: 'Exclude' } },
+ 'Current full text question?',
+ 'l2',
+ 'human',
+ ),
+ ).toEqual({ selected: 'Exclude' })
+ })
})
describe('ScreeningCitationContext', () => {
diff --git a/frontend/components/can-sr/screening-citation-context.tsx b/frontend/components/can-sr/screening-citation-context.tsx
index 99fb4bd8..1e504116 100644
--- a/frontend/components/can-sr/screening-citation-context.tsx
+++ b/frontend/components/can-sr/screening-citation-context.tsx
@@ -28,6 +28,70 @@ export function resolveConfiguredValue(
return matches.length === 1 ? row[matches[0]] : undefined
}
+function normalizeScreeningKey(value: string) {
+ return value
+ .trim()
+ .toLowerCase()
+ .replace(/[^a-z0-9]+/g, '_')
+ .replace(/^_+|_+$/g, '')
+ .replace(/_+/g, '_')
+}
+
+function legacyScreeningAnswerColumn(
+ question: string,
+ prefix: 'human' | 'llm',
+) {
+ const core = normalizeScreeningKey(question)
+ if (!core) return `${prefix}_col`
+ return `${prefix}_${core}`.slice(0, 60)
+}
+
+function screeningAnswerCandidateHeaders(
+ question: string,
+ stage: 'l1' | 'l2',
+ prefix: 'human' | 'llm',
+) {
+ const headers = [screeningAnswerColumn(question, prefix, stage)]
+ if (stage === 'l2') {
+ headers.push(screeningAnswerColumn(question, prefix, 'l1'))
+ }
+ headers.push(legacyScreeningAnswerColumn(question, prefix))
+ return headers
+}
+
+export function screeningAnswerColumn(
+ question: string,
+ prefix: 'human' | 'llm',
+ stage: 'l1' | 'l2',
+) {
+ const core = normalizeScreeningKey(question).slice(0, 56)
+ if (!core) return `${prefix}_col`
+ return `${prefix}_${stage}_${core}`.slice(0, 63)
+}
+
+export function resolveScreeningAnswerValue(
+ row: Record | null | undefined,
+ question: string,
+ stage: 'l1' | 'l2',
+ prefix: 'human' | 'llm',
+ fallbackHeaders: Array = [],
+) {
+ const headers = [
+ ...screeningAnswerCandidateHeaders(question, stage, prefix),
+ ...fallbackHeaders,
+ ].filter(
+ (header): header is string =>
+ typeof header === 'string' && header.trim().length > 0,
+ )
+
+ for (const header of headers) {
+ const value = resolveConfiguredValue(row, header)
+ if (value !== undefined) return value
+ }
+
+ return undefined
+}
+
export function extractHumanAnswer(value: any): string {
if (value === undefined || value === null) return ''
if (typeof value === 'object')
@@ -57,8 +121,25 @@ export function humanAnswerStatus(
answerColumn: string | null | undefined,
options: string[],
): 'unconfigured' | 'missing' | 'blank' | 'matched' | 'unmatched' {
- if (!answerColumn) return 'unconfigured'
- const raw = resolveConfiguredValue(row, answerColumn)
+ return humanAnswerStatusForColumns(row, [answerColumn], options)
+}
+
+export function humanAnswerStatusForColumns(
+ row: Record | null | undefined,
+ answerColumns: Array,
+ options: string[],
+): 'unconfigured' | 'missing' | 'blank' | 'matched' | 'unmatched' {
+ const headers = answerColumns.filter(
+ (header): header is string =>
+ typeof header === 'string' && header.trim().length > 0,
+ )
+ if (!headers.length) return 'unconfigured'
+
+ let raw: any = undefined
+ for (const header of headers) {
+ raw = resolveConfiguredValue(row, header)
+ if (raw !== undefined) break
+ }
if (raw === undefined) return 'missing'
const answer = extractHumanAnswer(raw).trim()
if (!answer) return 'blank'
@@ -71,6 +152,37 @@ export function humanAnswerStatus(
: 'unmatched'
}
+export function resolveConfiguredAnswerWarning(
+ row: Record | null | undefined,
+ answerColumn: string | null | undefined,
+ configuredAnswerColumn: string | null | undefined,
+ options: string[],
+): {
+ column: string
+ status: 'missing' | 'blank' | 'unmatched'
+} | null {
+ if (
+ !configuredAnswerColumn ||
+ configuredAnswerColumn.trim().length === 0 ||
+ configuredAnswerColumn === answerColumn
+ ) {
+ return null
+ }
+
+ const status = humanAnswerStatus(row, configuredAnswerColumn, options)
+ if (
+ status === 'unconfigured' ||
+ status === 'matched'
+ ) {
+ return null
+ }
+
+ return {
+ column: configuredAnswerColumn,
+ status,
+ }
+}
+
export function ScreeningCitationContext({
citation,
fields,