A Video-Based Procedure-Specific Competency Assessment Tool for Minimally Invasive Esophagectomy
Notice bibliographique
Résumé
Importance: Minimally invasive esophagectomy (MIE) is a complex procedure with substantial learning curves. In other complex minimally invasive procedures, suboptimal surgical performance has convincingly been associated with less favorable patient outcomes as assessed by peer review of the surgical procedure. Objective: To develop and validate a procedure-specific competency assessment tool (CAT) for MIE. Design, Setting, and Participants: In this international quality improvement study, a procedure-specific MIE-CAT was developed and validated. The MIE-CAT contains 8 procedural phases, and 4 quality components per phase are scored with a Likert scale ranging from 1 to 4. For evaluation of the MIE-CAT, intraoperative MIE videos performed by a single surgical team in the Esophageal Center East Netherlands were peer reviewed by 18 independent international MIE experts (with more than 120 MIEs performed). Each video was assessed by 2 or 3 blinded experts to evaluate feasibility, content validity, reliability, and construct validity. MIE-CAT version 2 was composed with refined content aimed at improving interrater reliability. A total of 32 full-length MIE videos from patients who underwent MIE between 2011 and 2020 were analyzed. Data were analyzed from January 2021 to January 2023. Exposure: Performance assessment of transthoracic MIE with an intrathoracic anastomosis. Main Outcomes and Measures: Feasibility, content validity, interrater and intrarater reliability, and construct validity, including correlations with both experience of the surgical team and clinical parameters, of the developed MIE-CAT. Results: Experts found the MIE-CAT easy to understand and easy to use to grade surgical performance. The MIE-CAT demonstrated good intrarater reliability (range of intraclass correlation coefficients [ICCs], 0.807 [95% CI, 0.656 to 0.892] for quality component score to 0.898 [95% CI, 0.846 to 0.932] for phase score). Interrater reliability was moderate (range of ICCs, 0.536 [95% CI, -0.220 to 0.994] for total MIE-CAT score to 0.705 [95% CI, 0.473 to 0.846] for quality component score), and most discrepancies originated in the lymphadenectomy phases. Hypothesis testing for construct validity showed more than 75% of hypotheses correct: MIE-CAT performance scores correlated with experience of the surgical team (r = 0.288 to 0.622), blood loss (r = -0.034 to -0.545), operative time (r = -0.309 to -0.611), intraoperative complications (r = -0.052 to -0.319), and severe postoperative complications (r = -0.207 to -0.395). MIE-CAT version 2 increased usability. Interrater reliability improved but remained moderate (range of ICCs, 0.666 to 0.743), and most discrepancies between raters remained in the lymphadenectomy phases. Conclusions and Relevance: The MIE-CAT was developed and its feasibility, content validity, reliability, and construct validity were demonstrated. By providing insight into surgical performance of MIE, the MIE-CAT might be used for clinical, training, and research purposes.
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 | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».