Discrepancy analysis comparing molecular and histology diagnoses in kidney transplant biopsies
Notice bibliographique
Résumé
Discrepancy analysis comparing two diagnostic platforms offers potential insights into both without assuming either is always correct. Having optimized the Molecular Microscope Diagnostic System (MMDx) in renal transplant biopsies, we studied discrepancies within MMDx (reports and sign-out comments) and between MMDx and histology. Interpathologist discrepancies have been documented previously and were not assessed. Discrepancy cases were classified as “clear” (eg, antibody-mediated rejection [ABMR] vs T cell–mediated rejection [TCMR]), “boundary” (eg, ABMR vs possible ABMR), or “mixed” (eg, Mixed vs ABMR). MMDx report scores showed 99% correlations; sign-out interpretations showed 7% variation between observers, all located around boundaries. Histology disagreed with MMDx in 37% of biopsies, including 315 clear discrepancies, all with implications for therapy. Discrepancies were distributed widely in all histology diagnoses but increased in some scenarios; for example, histology TCMR contained 14% MMDx ABMR and 20% MMDx no rejection. MMDx usually gave unambiguous diagnoses in cases with ambiguous histology, for example, borderline and transplant glomerulopathy. Histology lesions or features associated with more frequent discrepancies (eg, tubulitis, arteritis, and polyomavirus nephropathy) were not associated with increased MMDx uncertainty, indicating that MMDx can clarify biopsies with histologic ambiguity. The patterns of histology-MMDx discrepancies highlight specific histology diagnoses in which MMDx assessment should be considered for guiding therapy. Discrepancy analysis comparing two diagnostic platforms offers potential insights into both without assuming either is always correct. Having optimized the Molecular Microscope Diagnostic System (MMDx) in renal transplant biopsies, we studied discrepancies within MMDx (reports and sign-out comments) and between MMDx and histology. Interpathologist discrepancies have been documented previously and were not assessed. Discrepancy cases were classified as “clear” (eg, antibody-mediated rejection [ABMR] vs T cell–mediated rejection [TCMR]), “boundary” (eg, ABMR vs possible ABMR), or “mixed” (eg, Mixed vs ABMR). MMDx report scores showed 99% correlations; sign-out interpretations showed 7% variation between observers, all located around boundaries. Histology disagreed with MMDx in 37% of biopsies, including 315 clear discrepancies, all with implications for therapy. Discrepancies were distributed widely in all histology diagnoses but increased in some scenarios; for example, histology TCMR contained 14% MMDx ABMR and 20% MMDx no rejection. MMDx usually gave unambiguous diagnoses in cases with ambiguous histology, for example, borderline and transplant glomerulopathy. Histology lesions or features associated with more frequent discrepancies (eg, tubulitis, arteritis, and polyomavirus nephropathy) were not associated with increased MMDx uncertainty, indicating that MMDx can clarify biopsies with histologic ambiguity. The patterns of histology-MMDx discrepancies highlight specific histology diagnoses in which MMDx assessment should be considered for guiding therapy.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».