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Record W4390275428 · doi:10.1001/jamasurg.2023.6522

A Video-Based Procedure-Specific Competency Assessment Tool for Minimally Invasive Esophagectomy

2023· letter· en· W4390275428 on OpenAlexaff
Mirte H. M. Ketel, Bastiaan Klarenbeek, Yassin Eddahchouri, Edward Cheong, Miguel A. Cuesta, Elke Van Daele, Lorenzo Ferri, Suzanne S. Gisbertz, Christian A. Gutschow, Michal Hubka, A. H. Hölscher, Simon Law, Misha Luyer, Robert E. Merritt, Christopher R. Morse, Carmen Mueller, Grard A. P. Nieuwenhuijzen, Magnus Nilsson, Piet Pattyn, Yaxing Shen, Frits J. H. van den Wildenberg, Inger L. Abma, Camiel Rosman, Frans van Workum

Bibliographic record

VenueJAMA Surgery · 2023
Typeletter
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsMedicineInter-rater reliabilityIntra-rater reliabilityIntraclass correlationConstruct validityReliability (semiconductor)Mie scatteringSurgeryMedical physicsRating scalePatient satisfactionInternal medicinePsychometrics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.046
GPT teacher head0.322
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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