A Video-Based Procedure-Specific Competency Assessment Tool for Minimally Invasive Esophagectomy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".