chore: sauvegarde état courant avant merge des branches teammates

Modifications en cours : pipeline médical (cim10_extractor, dp_finalizer,
dp_selector, fusion, rag_search), viewer (helpers, detail.html),
cache ollama et référentiels.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

data/referentiels/user/index.json

@@ -318,5 +318,105 @@
     "date_added": "2026-03-07T21:20:31.742855",
     "status": "uploaded",
     "chunks_count": 0
+  },
+  {
+    "id": "e3f31431f656",
+    "filename": "doc.txt",
+    "stored_name": "user/e3f31431f656_doc.txt",
+    "extension": ".txt",
+    "size_bytes": 12,
+    "date_added": "2026-03-08T11:46:02.617694",
+    "status": "uploaded",
+    "chunks_count": 0
+  },
+  {
+    "id": "2ee6fb01e98f",
+    "filename": "doc.txt",
+    "stored_name": "user/2ee6fb01e98f_doc.txt",
+    "extension": ".txt",
+    "size_bytes": 12,
+    "date_added": "2026-03-08T11:52:46.222372",
+    "status": "uploaded",
+    "chunks_count": 0
+  },
+  {
+    "id": "69d6c1a41527",
+    "filename": "doc.txt",
+    "stored_name": "user/69d6c1a41527_doc.txt",
+    "extension": ".txt",
+    "size_bytes": 12,
+    "date_added": "2026-03-08T11:57:59.137425",
+    "status": "uploaded",
+    "chunks_count": 0
+  },
+  {
+    "id": "403b5641292f",
+    "filename": "doc.txt",
+    "stored_name": "user/403b5641292f_doc.txt",
+    "extension": ".txt",
+    "size_bytes": 12,
+    "date_added": "2026-03-08T12:06:01.501324",
+    "status": "uploaded",
+    "chunks_count": 0
+  },
+  {
+    "id": "7cc01afb71a0",
+    "filename": "doc.txt",
+    "stored_name": "user/7cc01afb71a0_doc.txt",
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+    "size_bytes": 12,
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+    "size_bytes": 12,
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+    "status": "uploaded",
+    "chunks_count": 0
+  },
+  {
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+    "filename": "doc.txt",
+    "stored_name": "user/177c5fb11e54_doc.txt",
+    "extension": ".txt",
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+    "status": "uploaded",
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+    "filename": "doc.txt",
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+    "status": "uploaded",
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+    "status": "uploaded",
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+    "filename": "doc.txt",
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+    "extension": ".txt",
+    "size_bytes": 12,
+    "date_added": "2026-03-08T12:36:28.174289",
+    "status": "uploaded",
+    "chunks_count": 0
   }
 ]

src/medical/cim10_extractor.py

@@ -211,6 +211,9 @@ def extract_medical_info(
     # Post-processing : calcul DFG et détection IRC non codée
     _check_dfg_irc(dossier)
 
+    # Post-processing : auto-codage anomalies biologiques évidentes
+    _auto_code_bio_anomalies(dossier)
+
     # Post-processing : détection erreurs fréquentes (intuition DIM senior)
     try:
         from .dim_senior import check_common_mistakes
@@ -247,13 +250,15 @@ def extract_medical_info(
 
 
 def _check_dfg_irc(dossier: DossierMedical) -> None:
-    """Calcule le DFG si créatinine disponible et alerte si IRC non codée."""
+    """Calcule le DFG si créatinine disponible et alerte si IRC non codée.
+
+    Si âge/sexe manquants (données anonymisées), détecte quand même l'IRA
+    à partir d'une créatinine très élevée (> 150 µmol/L).
+    """
     from .bio_normals import compute_dfg, stade_irc
 
     age = dossier.sejour.age if dossier.sejour else None
     sexe = dossier.sejour.sexe if dossier.sejour else None
-    if not age or not sexe or age < 18:
-        return
 
     # Trouver la créatinine
     creat_val = None
@@ -264,6 +269,33 @@ def _check_dfg_irc(dossier: DossierMedical) -> None:
     if creat_val is None:
         return
 
+    # Codes rénaux déjà présents
+    all_codes = set()
+    if dossier.diagnostic_principal and dossier.diagnostic_principal.cim10_suggestion:
+        all_codes.add(dossier.diagnostic_principal.cim10_suggestion)
+    for das in dossier.diagnostics_associes:
+        if das.cim10_suggestion:
+            all_codes.add(das.cim10_suggestion)
+
+    # Si âge/sexe manquants → pas de DFG, mais auto-code IRA si créatinine très haute
+    if not age or not sexe or age < 18:
+        if creat_val > 150:
+            n17 = any(c.startswith("N17") for c in all_codes)
+            n18 = any(c.startswith("N18") for c in all_codes)
+            n19 = any(c.startswith("N19") for c in all_codes)
+            if not n17 and not n18 and not n19:
+                dossier.diagnostics_associes.append(Diagnostic(
+                    texte="Insuffisance rénale aiguë",
+                    cim10_suggestion="N17.9",
+                    cim10_confidence="medium",
+                    justification=f"Créatinine {creat_val} µmol/L > 150 (âge inconnu, DFG non calculable)",
+                    source="auto_bio",
+                ))
+                dossier.alertes_codage.append(
+                    f"AUTO-CODE IRA: N17.9 ajouté — Créatinine {creat_val} µmol/L > 150"
+                )
+        return
+
     dfg = compute_dfg(creat_val, age, sexe)
     if dfg is None:
         return
@@ -273,22 +305,79 @@ def _check_dfg_irc(dossier: DossierMedical) -> None:
     # Vérifier si IRC codée alors que DFG le justifie
     if dfg < 60:
         stade, code_attendu = stade_irc(dfg)
-        # Chercher si un N18.x est déjà codé
+        irc_coded = any(c.startswith("N18") for c in all_codes)
-        irc_coded = False
+
+        if not irc_coded:
+            dossier.diagnostics_associes.append(Diagnostic(
+                texte=f"Insuffisance rénale chronique stade {stade}",
+                cim10_suggestion=code_attendu,
+                cim10_confidence="high",
+                justification=f"DFG estimé {dfg} mL/min (créatinine {creat_val} µmol/L, {sexe} {age} ans)",
+                source="auto_bio",
+            ))
+            dossier.alertes_codage.append(
+                f"AUTO-CODE IRC: {code_attendu} ajouté (DFG {dfg} mL/min, stade {stade}) — "
+                f"Créatinine {creat_val} µmol/L, {sexe} {age} ans."
+            )
+            dossier.quality_flags["irc_auto_codee"] = code_attendu
+
+
+# Mapping : (analyte, direction) → (code CIM-10, texte, seuil_critique)
+# Seuil critique = valeur à partir de laquelle le codage est quasi-certain
+_BIO_AUTO_CODE: list[tuple[str, str, str, str, float | None]] = [
+    # analyte, direction, code, texte, seuil_critique (None = toute anomalie)
+    ("Hémoglobine", "low", "D64.9", "Anémie", 10.0),          # Hb < 10 g/dL
+    ("Potassium", "high", "E87.5", "Hyperkaliémie", 5.5),     # K > 5.5 mmol/L
+    ("Potassium", "low", "E87.6", "Hypokaliémie", 3.0),       # K < 3.0 mmol/L
+    ("Sodium", "low", "E87.1", "Hyponatrémie", 130.0),        # Na < 130 mmol/L
+    ("Plaquettes", "low", "D69.6", "Thrombopénie", 100.0),    # Plq < 100 G/L
+]
+
+
+def _auto_code_bio_anomalies(dossier: DossierMedical) -> None:
+    """Auto-code des DAS quand des anomalies biologiques franches ne sont pas codées."""
+    from .bio_normals import _is_abnormal
+
     all_codes = set()
     if dossier.diagnostic_principal and dossier.diagnostic_principal.cim10_suggestion:
         all_codes.add(dossier.diagnostic_principal.cim10_suggestion)
     for das in dossier.diagnostics_associes:
         if das.cim10_suggestion:
             all_codes.add(das.cim10_suggestion)
-        irc_coded = any(c.startswith("N18") for c in all_codes)
 
-        if not irc_coded:
+    age = dossier.sejour.age if dossier.sejour else None
+    sexe = dossier.sejour.sexe if dossier.sejour else None
+
+    for bio in dossier.biologie_cle:
+        if not bio.valeur_num:
+            continue
+        is_abn = _is_abnormal(bio.test, bio.valeur, age, sexe)
+        if not is_abn:
+            continue
+
+        for analyte, direction, code, texte, seuil in _BIO_AUTO_CODE:
+            if bio.test != analyte:
+                continue
+            # Vérifier la direction (high/low)
+            if direction == "low" and seuil is not None and bio.valeur_num >= seuil:
+                continue
+            if direction == "high" and seuil is not None and bio.valeur_num <= seuil:
+                continue
+            # Vérifier que le code n'est pas déjà codé (exact ou famille 3 chars)
+            if any(c.startswith(code[:3]) for c in all_codes):
+                continue
+            # Auto-coder
+            dossier.diagnostics_associes.append(Diagnostic(
+                texte=texte,
+                cim10_suggestion=code,
+                cim10_confidence="high",
+                justification=f"{bio.test} = {bio.valeur} (seuil auto-code: {seuil})",
+                source="auto_bio",
+            ))
+            all_codes.add(code)
             dossier.alertes_codage.append(
-                f"DFG estimé {dfg} mL/min (stade {stade}) — IRC ({code_attendu}) "
+                f"AUTO-CODE BIO: {code} ({texte}) ajouté — {bio.test} = {bio.valeur}"
-                f"non codée. Créatinine {creat_val} µmol/L, {sexe} {age} ans."
             )
-            dossier.quality_flags["irc_non_codee"] = code_attendu
 
 
 def _extract_das_llm(text: str, dossier: DossierMedical) -> None:

src/medical/dp_finalizer.py

@@ -320,6 +320,38 @@ def finalize_dp(dossier: DossierMedical) -> DossierMedical:
     # ── 3. Arbitrage ───────────────────────────────────────────────
     dp_final, flags, alertes = decide_dp_final(trackare_dp, crh_dp)
 
+    # ── 3b. R6 — Z-code non whitelisté en DP : promotion DAS ──────
+    final_code = (dp_final.chosen_code or "").upper()
+    if (
+        final_code.startswith("Z")
+        and _family3(final_code) not in _Z_CODE_DP_WHITELIST
+        and dossier.diagnostics_associes
+    ):
+        best_das = None
+        for das in dossier.diagnostics_associes:
+            c = (das.cim10_suggestion or "").upper()
+            if not c or c.startswith("R") or c.startswith("Z"):
+                continue
+            if das.cim10_confidence in ("high", "medium"):
+                best_das = das
+                break
+        if best_das:
+            old_code = dp_final.chosen_code
+            dp_final.chosen_code = best_das.cim10_suggestion
+            dp_final.chosen_term = best_das.texte
+            dp_final.verdict = "REVIEW"
+            dp_final.confidence = "medium"
+            dp_final.evidence.append(
+                f"R6 — Z-code {old_code} remplacé par DAS {best_das.cim10_suggestion} "
+                f"({best_das.texte[:40]})"
+            )
+            dp_final.reason = (dp_final.reason or "") + " | R6 Z-code→DAS"
+            flags["z_code_replaced_by_das"] = True
+            alertes.append(
+                f"Z-code {old_code} inadapté en DP → remplacé par "
+                f"{best_das.cim10_suggestion} ({best_das.texte[:40]})"
+            )
+
     # ── 4. Écrire les résultats ────────────────────────────────────
     dossier.dp_final = dp_final
 

src/medical/dp_selector.py

@@ -256,15 +256,15 @@ def score_candidates(
             score += _diag_section_bonus
             details["diag_section_bonus"] = _diag_section_bonus
 
-        # 5. Malus comorbidité chronique
+        # 5. Malus comorbidité chronique (réduit : les comorbidités sont parfois DP légitimes)
         if c.is_comorbidity_like:
-            score -= 3
+            score -= 1.5
-            details["comorbidity_malus"] = -3
+            details["comorbidity_malus"] = -1.5
 
-        # 6. Malus symptôme (R-code)
+        # 6. Malus symptôme (R-code) — renforcé : un symptôme est rarement le DP
         if c.is_symptom_like:
-            score -= 2
+            score -= 3
-            details["symptom_malus"] = -2
+            details["symptom_malus"] = -3
 
         # 7. Malus acte-seul
         if c.is_act_only:
@@ -579,6 +579,23 @@ def select_dp(
             reason="Aucun candidat DP identifié",
         )
 
+    # Garde-fou R-code : si le top-1 est un R-code et qu'il existe un candidat
+    # non-symptôme ET non-comorbidité, forcer sa promotion
+    if (len(candidates) >= 2
+            and _is_symptom_like(candidates[0].code)):
+        best_diag = next(
+            (c for c in candidates
+             if not _is_symptom_like(c.code) and not _is_comorbidity_like(c.code)),
+            None,
+        )
+        if best_diag and best_diag is not candidates[0]:
+            logger.info(
+                "NUKE-3: R-code %s rétrogradé, promotion de %s (%s)",
+                candidates[0].code, best_diag.term, best_diag.code,
+            )
+            candidates.remove(best_diag)
+            candidates.insert(0, best_diag)
+
     # Candidat unique → CONFIRMED (sous réserve hardening A2)
     if len(candidates) == 1:
         c = candidates[0]

src/medical/edsnlp_pipeline.py

@@ -83,10 +83,38 @@ def get_pipeline():
 def analyze(text: str) -> EdsnlpResult:
     """Analyse un texte médical avec edsnlp.
 
+    Essaie le serveur distant d'abord, puis fallback local.
     Retourne les entités CIM-10, médicaments et dates détectées.
     """
     result = EdsnlpResult()
 
+    # Essayer le serveur distant d'abord
+    try:
+        from .remote_embed import ner_remote
+        remote = ner_remote(text)
+        if remote is not None and "error" not in remote:
+            for ent in remote.get("cim10", []):
+                result.cim10_entities.append(CIM10Entity(
+                    texte=ent["text"], code=ent["code"],
+                    negation=ent.get("negation", False),
+                    hypothese=ent.get("hypothesis", False),
+                ))
+            for ent in remote.get("drugs", []):
+                result.drug_entities.append(DrugEntity(
+                    texte=ent["text"], code_atc=ent.get("code_atc"),
+                    negation=ent.get("negation", False),
+                ))
+            for ent in remote.get("dates", []):
+                result.date_entities.append(DateEntity(
+                    texte=ent["text"], value=ent.get("value"),
+                ))
+            logger.debug("edsnlp distant: %d CIM-10, %d drugs, %.0fms",
+                        len(result.cim10_entities), len(result.drug_entities),
+                        remote.get("time_ms", 0))
+            return result
+    except ImportError:
+        pass
+
     if not is_available():
         return result
 

src/medical/fusion.py

@@ -35,22 +35,47 @@ def _cim10_specificity(code: str | None) -> int:
     return len(code.replace(".", ""))
 
 
+def _dp_sort_score(diag: Diagnostic, doc_type: str) -> tuple[int, int, int, int]:
+    """Score de tri pour sélection DP fusionné. Plus petit = meilleur.
+
+    Priorité :
+    1. Pénalité Z/R codes (vrais diagnostics d'abord)
+    2. Bonus trackare (codage DIM établissement)
+    3. Spécificité CIM-10 décroissante
+    4. Confiance (high > medium > low)
+    """
+    code = (diag.cim10_suggestion or "").upper()
+    # Pénalité Z/R : 0=diagnostic, 1=R-code, 2=Z-code
+    zr_penalty = 0
+    if code.startswith("Z"):
+        zr_penalty = 2
+    elif code.startswith("R"):
+        zr_penalty = 1
+    # Bonus trackare : 0=trackare, 1=autre
+    trackare_bonus = 0 if doc_type == "trackare" else 1
+    # Spécificité inverse (négatif pour tri ascendant)
+    spec = -_cim10_specificity(code)
+    # Confiance
+    conf_order = {"high": 0, "medium": 1, "low": 2}
+    conf = conf_order.get(diag.cim10_confidence or "", 3)
+    return (zr_penalty, spec, conf, trackare_bonus)
+
+
 def _prefer_most_specific_dp(dossiers: list[DossierMedical]) -> Diagnostic | None:
-    """Sélectionne le DP le plus spécifique parmi tous les dossiers."""
+    """Sélectionne le meilleur DP parmi tous les dossiers.
-    candidates: list[tuple[Diagnostic, int]] = []
+
+    Préfère diagnostics réels > R-codes > Z-codes, trackare > CRH,
+    puis spécificité CIM-10 décroissante.
+    """
+    candidates: list[tuple[Diagnostic, str]] = []
     for d in dossiers:
-        if d.diagnostic_principal:
+        if d.diagnostic_principal and d.diagnostic_principal.cim10_suggestion:
-            spec = _cim10_specificity(d.diagnostic_principal.cim10_suggestion)
+            candidates.append((d.diagnostic_principal, d.document_type or ""))
-            candidates.append((d.diagnostic_principal, spec))
 
     if not candidates:
         return None
 
-    # Tri : spécificité décroissante, puis confiance (high > medium > low)
+    candidates.sort(key=lambda x: _dp_sort_score(x[0], x[1]))
-    conf_order = {"high": 0, "medium": 1, "low": 2}
-    candidates.sort(
-        key=lambda x: (-x[1], conf_order.get(x[0].cim10_confidence or "", 3))
-    )
     return candidates[0][0]
 
 
@@ -189,15 +214,56 @@ def merge_dossiers(dossiers: list[DossierMedical]) -> DossierMedical:
     merged.diagnostic_principal = _prefer_most_specific_dp(dossiers)
 
     # Propager dp_selection depuis le dossier source du DP retenu
+    from ..config import DPSelection
     if merged.diagnostic_principal:
         dp_code = merged.diagnostic_principal.cim10_suggestion
         for d in dossiers:
             if (d.diagnostic_principal
-                    and d.diagnostic_principal.cim10_suggestion == dp_code
+                    and d.diagnostic_principal.cim10_suggestion == dp_code):
-                    and d.dp_selection is not None):
+                if d.dp_selection is not None:
                     merged.dp_selection = d.dp_selection
+                else:
+                    # Créer un dp_selection synthétique pour le finalizer
+                    merged.dp_selection = DPSelection(
+                        chosen_code=dp_code,
+                        chosen_term=d.diagnostic_principal.texte,
+                        verdict="CONFIRMED",
+                        confidence=d.diagnostic_principal.cim10_confidence or "medium",
+                        evidence=[f"Source: {d.document_type or 'fusion'}"],
+                        reason=f"DP {d.document_type or 'fusion'} (synthétique)",
+                    )
                 break
 
+    # Propager les dp_selection trackare et CRH séparément pour le finalizer
+    for d in dossiers:
+        if d.document_type == "trackare":
+            if merged.dp_trackare is None:
+                if d.dp_selection is not None:
+                    merged.dp_trackare = d.dp_selection
+                elif d.diagnostic_principal and d.diagnostic_principal.cim10_suggestion:
+                    # Créer un DPSelection synthétique depuis le DP trackare
+                    merged.dp_trackare = DPSelection(
+                        chosen_code=d.diagnostic_principal.cim10_suggestion,
+                        chosen_term=d.diagnostic_principal.texte,
+                        verdict="CONFIRMED",
+                        confidence=d.diagnostic_principal.cim10_confidence or "medium",
+                        evidence=["Source: Trackare (codage établissement)"],
+                        reason="DP Trackare (synthétique)",
+                    )
+        else:
+            if merged.dp_crh_only is None:
+                if d.dp_selection is not None:
+                    merged.dp_crh_only = d.dp_selection
+                elif d.diagnostic_principal and d.diagnostic_principal.cim10_suggestion:
+                    merged.dp_crh_only = DPSelection(
+                        chosen_code=d.diagnostic_principal.cim10_suggestion,
+                        chosen_term=d.diagnostic_principal.texte,
+                        verdict="CONFIRMED",
+                        confidence=d.diagnostic_principal.cim10_confidence or "medium",
+                        evidence=["Source: CRH (analyse pipeline)"],
+                        reason="DP CRH (synthétique)",
+                    )
+
     # Collecter tous les DAS + DP non retenus comme DAS
     all_das: list[Diagnostic] = []
     for d in dossiers:

src/medical/ollama_client.py

@@ -149,7 +149,7 @@ def parse_json_response(raw: str) -> dict | None:
 def call_ollama(
     prompt: str,
     temperature: float = 0.1,
-    max_tokens: int = 2500,
+    max_tokens: int = 4000,
     model: str | None = None,
     timeout: int | None = None,
     role: str | None = None,

src/medical/rag_search.py

@@ -122,10 +122,22 @@ def _rerank(query: str, results: list[dict], top_k: int) -> list[dict]:
     if not results:
         return results
 
-    reranker = _get_reranker()
+    passages = [r.get("extrait", "") for r in results]
 
-    # Construire les paires (query, passage) pour le cross-encoder
+    # Essayer le serveur distant d'abord
-    pairs = [(query, r.get("extrait", "")) for r in results]
+    ce_scores = None
+    try:
+        from .remote_embed import rerank_remote
+        remote_scores = rerank_remote(query, passages)
+        if remote_scores is not None:
+            ce_scores = remote_scores
+    except ImportError:
+        pass
+
+    # Fallback local
+    if ce_scores is None:
+        reranker = _get_reranker()
+        pairs = [(query, p) for p in passages]
         ce_scores = reranker.predict(pairs)
 
     # Injecter le score cross-encoder et trier
@@ -138,10 +150,9 @@ def _rerank(query: str, results: list[dict], top_k: int) -> list[dict]:
 
 
 def _embed_cached(texts: list[str]) -> "numpy.ndarray":
-    """Calcule les embeddings avec cache. Retourne un array (N, dim)."""
+    """Calcule les embeddings avec cache. Essaie le serveur distant d'abord."""
     import numpy as np
 
-    model = _get_embed_model()
     results = [None] * len(texts)
     to_compute: list[tuple[int, str]] = []
 
@@ -155,6 +166,18 @@ def _embed_cached(texts: list[str]) -> "numpy.ndarray":
 
     if to_compute:
         new_texts = [t for _, t in to_compute]
+
+        # Essayer le serveur distant d'abord
+        new_vecs = None
+        try:
+            from .remote_embed import embed_remote
+            new_vecs = embed_remote(new_texts)
+        except ImportError:
+            pass
+
+        # Fallback local
+        if new_vecs is None:
+            model = _get_embed_model()
             new_vecs = model.encode(new_texts, normalize_embeddings=True, batch_size=64)
             new_vecs = np.array(new_vecs, dtype=np.float32)
 
@@ -703,6 +726,36 @@ def enrich_acte(acte: ActeCCAM, contexte: dict, cache: OllamaCache | None = None
         logger.info("Ollama non disponible — sources FAISS CCAM conservées sans justification LLM")
 
 
+def _smart_truncate(text: str, max_chars: int = 6000) -> str:
+    """Troncature intelligente : garde le début + les sections finales importantes.
+
+    Pour les textes longs, on garde :
+    - Les premiers 60% de max_chars (début du document : identité, motif, histoire)
+    - Les derniers 40% (conclusion, synthèse, diagnostic de sortie, TTT)
+    Séparés par un marqueur [...] pour indiquer la troncature.
+    """
+    if len(text) <= max_chars:
+        return text
+
+    head_size = int(max_chars * 0.6)
+    tail_size = max_chars - head_size - 30  # 30 chars pour le séparateur
+
+    # Chercher une fin de phrase propre pour le head
+    head = text[:head_size]
+    last_newline = head.rfind("\n")
+    if last_newline > head_size * 0.8:
+        head = head[:last_newline]
+
+    # Chercher un début de ligne propre pour le tail
+    tail_start = len(text) - tail_size
+    first_newline = text.find("\n", tail_start)
+    if first_newline > 0 and first_newline < tail_start + 200:
+        tail_start = first_newline + 1
+    tail = text[tail_start:]
+
+    return head + "\n\n[... texte tronqué ...]\n\n" + tail
+
+
 def _build_prompt_das_extraction(text: str, contexte: dict, existing_das: list[str], dp_texte: str) -> str:
     """Construit le prompt pour l'extraction LLM de DAS supplémentaires."""
     ctx_str = format_enriched_context(contexte)
@@ -712,7 +765,7 @@ def _build_prompt_das_extraction(text: str, contexte: dict, existing_das: list[s
         dp_texte=dp_texte or "Non identifié",
         existing_str=existing_str,
         ctx_str=ctx_str,
-        text_medical=text[:4000],
+        text_medical=_smart_truncate(text, 6000),
     )
 
 

src/viewer/helpers.py

@@ -678,11 +678,25 @@ def _get_system_status() -> list[dict]:
 
 
 def _sort_qc_alerts(alerts: list[str]) -> list[str]:
+    """Trie les alertes QC : problèmes en haut, positif/recommandations en bas."""
     def _key(a: str) -> tuple[int, int]:
         text = a.lower()
+        # Priorité 0 : erreurs critiques, incohérences, données aberrantes
+        if any(k in text for k in ("erreur", "critique", "aberrant", "incompatible", "incohéren")):
+            tier = 0
+        # Priorité 1 : redondances, confusions, à reconsidérer
+        elif any(k in text for k in ("redondance", "confusion", "reconsidérer", "reconsider", "high→low", "high → low", "flou")):
+            tier = 1
+        # Priorité 2 : recommandations, demandes de clarification
+        elif any(k in text for k in ("recommandation", "demander", "clarification", "vérifier")):
+            tier = 2
+        # Priorité 3 : éléments positifs (codes justifiés, etc.)
+        elif any(k in text for k in ("justifié", "solidement", "conforme", "validé")):
+            tier = 3
+        else:
+            tier = 2
         dp = 0 if " dp " in text or text.startswith("dp ") or "diagnostic principal" in text else 1
-        critical = 0 if any(k in text for k in ("high→low", "high → low", "à reconsidérer", "reconsider")) else 1
+        return (tier, dp)
-        return (dp, critical)
     return sorted(alerts, key=_key)
 
 

src/viewer/templates/detail.html

@@ -13,9 +13,15 @@
 {% set vr = dossier.veto_report %}
 
 <div class="card" style="margin-top:1rem;padding:1.25rem 1.5rem;">
-  {# Titre patient #}
+  {# Titre patient + identifiants #}
   <div style="display:flex;align-items:baseline;gap:0.75rem;margin-bottom:1rem;flex-wrap:wrap;">
     <h2 style="margin:0;">{{ current_group | format_dossier_name if current_group else (dossier.source_file or filepath) }}</h2>
+    <span class="badge" style="background:#e0e7ff;color:#3730a3;font-size:0.75rem;font-weight:600;" title="Identifiant du dossier dans le système">N° {{ current_group | format_dossier_name if current_group else filepath }}</span>
+    {% if dossier.controles_cpam %}
+      {% for ctrl in dossier.controles_cpam %}
+        <span class="badge" style="background:#fef3c7;color:#92400e;font-weight:700;font-size:0.8rem;" title="Numéro OGC (Ordonnance de Gestion de Caisse) du contrôle CPAM">OGC {{ ctrl.numero_ogc }}</span>
+      {% endfor %}
+    {% endif %}
     {% if dossier.document_type %}
       <span class="badge" style="background:#e0e7ff;color:#3730a3;" title="Type de document source (CRH, lettre de sortie, compte-rendu opératoire, etc.)">{{ dossier.document_type }}</span>
     {% endif %}
@@ -290,8 +296,8 @@
 
 {# ---- Diagnostics associés ---- #}
 {% if dossier.diagnostics_associes %}
-<div class="card section">
+<details class="card section">
-  <h3>Diagnostics associés ({{ dossier.diagnostics_associes|length }})</h3>
+  <summary><h3 style="display:inline;">Diagnostics associés ({{ dossier.diagnostics_associes|length }})</h3></summary>
   <table>
     <thead><tr><th title="Code CIM-10 attribué au diagnostic associé">Code CIM-10</th><th>Libellé</th><th title="Comorbidité/Morbidité Associée — indique si ce diagnostic augmente la sévérité GHM">CMA</th><th title="Niveau de confiance du pipeline IA sur ce code CIM-10">Confiance</th><th title="Source du diagnostic dans le document (page)">Source</th></tr></thead>
     <tbody>
@@ -386,7 +392,7 @@
     {% endfor %}
     </tbody>
   </table>
-</div>
+</details>
 {% endif %}
 
 {# ---- Actes CCAM ---- #}
@@ -423,7 +429,7 @@
 {# ==================================================================== #}
 {# 3. CONTRÔLE QUALITÉ CODAGE (section repliable)                       #}
 {# ==================================================================== #}
-<details open class="card section" style="margin-top:1rem;">
+<details class="card section" style="margin-top:1rem;">
   <summary><h3 style="display:inline;">Contrôle Qualité Codage</h3></summary>
 
   <div style="display:grid;grid-template-columns:repeat(auto-fit,minmax(300px,1fr));gap:1rem;margin-top:0.75rem;">

t2a-viewer.service

@@ -9,8 +9,8 @@ User=dom
 WorkingDirectory=/home/dom/ai/t2a_v2
 EnvironmentFile=/home/dom/ai/t2a_v2/.env
 ExecStart=/home/dom/ai/t2a/.venv/bin/gunicorn -c gunicorn.conf.py "src.viewer.app:create_app()"
-Restart=on-failure
+Restart=always
-RestartSec=5
+RestartSec=10
 StandardOutput=journal
 StandardError=journal