Endoscopy and histology in inflammatory bowel diseases patients: Complementary or alternatives?—Author’s reply
Notice bibliographique
Résumé
We read with great interest the editorial: “Endoscopy and histology in inflammatory bowel disease patients: complementary or alternatives?”1 and we thank Dr. D’Amico et al. for insightful comments. The clinical relevance of an endpoint that combines histopathological and endoscopic assessments of mucosal healing versus only endoscopic endpoint is a matter of increasing debate. We have reported that PICaSSO endoscopic score can accurately predict histologic remission without having to combine both measures and this is of significant practical benefit.2 However, we fully agree that we need randomized controlled trials before translating into routine clinical practice. In addition, we were aware of limited use of PICaSSO score given that this was initially, developed and validated using iSCAN platform (Pentax) which is not available in all the endoscopy unit.3, 4 To overcome this limitation, we have recently investigated PICaSSO reproducibility and validation by using narrow band imaging (NBI) Olympus and linked colour imaging/blue-laser imaging (LCI/BLI), Fujifilm and this has been recently published.5 After a brief training for PICaSSO, we determined the interobserver variability (ICC) in a group of both experienced and less experienced endoscopists who were asked to score colonoscopies videos. In both Virtual Electronic Chromoendoscopy (VCE) platforms (NBI, LCI/BLI) the ICC for PICaSSO and its subscores (mucosal and vascular) were either good or very good, and most importantly, always numerically higher than for Mayo Endoscopic Score (MES) and Ulcerative colitis (UC) endoscopic Index of severity (UCEIS). Furthermore, in both NBI and BLI/LCI groups PICaSSO showed a strong correlation with histology. These results confirm that PICaSSO is accurate and reliable score on all available VCE platforms.5 However, we have already previously tested interobserver agreement in experienced consultant and trainees who had no prior exposure to Electronic Virtual Chromoendoscopy, by using a short training module (colonoscopy video library: 30 cases reviewed pre-training and 30 post-training) and PICaSSO revealed good interobserver agreement across all levels of experience in even non-expert setting reaching high ICC.3 Notably training modules, representative of all the endoscopic mucosal and vascular findings and with all the endoscopy platforms, are now available.3, 5 In conclusion, PICaSSO made an important point in the current debate that using high-definition endoscopes and VCE to enhance mucosal and vascular details, discrepancy between endoscopy and histology has become small. Histology plays still a crucial role for the clinical management of UC and we should not think that endoscopy will replace histology but be complementary. However, we hope to pave the way to motivate the implementation of VCE with targeted “smart biopsies “ and eliminate the use of MES = 1 as endoscopic remission which is imprecise. Finally, PICASSO is an accurate endoscopic score and each of mucosal and vascular items describe a single features of healing and active inflammation. Hence is suitable for a computer aided diagnosis for standardisation and endoscopy reading.6 MI is part-funded by the NIHR Birmingham Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. None. Data available on request from the authors.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,025 | 0,032 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,006 |
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 source (Gemma direct ou Codex distillé), 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 ».