Endoscopic Stigmata: Recognition Lies in the Eye of the Beholder?
Notice bibliographique
Résumé
The most widely accepted classification for endoscopic stigmata is the Forrest classification, which divides endoscopically visible lesions into 3 classes (ie, I, II, and III). The classification is most useful because it is based on risk of rebleeding in the absence of endoscopic therapy. But how good are we at identifying/classifying the stigmata on endoscopy? Data from the REASON Registry from Canada, which reviewed endoscopy data retrospectively, showed that high-risk stigmata were present in 47.8% of the patients on endoscopy, but less than two-thirds of these received endoscopic therapy. On the other hand, about 9.8% patients with low-risk stigmata received endoscopic therapy. More than one-quarter of the reports did not document and classify the stigmata. Similar studies by Lau et al and Bour et al have shown poor inter-observer agreement between experts for recognition of endoscopic stigmata. An anonymous online survey was designed and separate links to the survey were e-mailed to faculty and fellows. We divided the survey into 2 parts—stigmata recognition and stigmata therapy. Under stigmata recognition, a standard image of a lesion belonging to one class of the Forrest classification was presented. The images were reproduced from records of our own endoscopies or from standard online digital libraries of endoscopic stigmata. The images were chosen to represent a very standard imagery representative of the lesion. Under stigmata therapy, we used different labeled images of endoscopic lesions and asked faculty/fellows to choose how they would treat the lesion endoscopically, including an option to not treat. Total of 17 faculty members and 11 fellows participated in the survey. Our study brings forth a few vital observations:1.Based on our data, overall low-risk stigmata was misclassified as high risk about 14% of time by board-certified gastroenterologists. This would have inadvertently led to unnecessary endoscopic therapy. In the same vein, high-risk stigmata was misclassified as low risk 6% of the time, putting patients at risk for rebleeding from them.2.Overall inter-observer agreement in recognition of stigmata was very good (κ = .85). It was poorest for an adherent clot (κ = .52) and best for spurting hemorrhage (κ = 1).3.Overall inter-observer agreement in treatment of endoscopic stigmata was good (κ = .74). It was again poorest for an adherent clot (κ = .58) and best for spurting hemorrhage/clean-based ulcer and flat pigmented spot (κ = 1).4.The κ indexes were significantly better for faculty with >3 years of experience vs junior fellows (first- and second-year fellows) (senior faculty overall κ = .85 ± .09 and junior fellows .68 ± .16; 2-tailed P = .047).
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 | 0,011 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,004 | 0,015 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,002 |
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 ».