Objective Assessment of Technical Skills in Otorhinolaryngology–Head and Neck Surgery Residents: A Systematic Review
Bibliographic record
Abstract
Objectives The primary goal is the indexation of validated methods used to assess surgical competency in otorhinolaryngology-head and neck surgery (ORL-HNS) residents. Secondary goals include assessment of the reliability and validity of these tools, as well as the documentation of specific procedures in ORL-HNS involved. Data Sources MEDBASE, OVID, Medline, CINAHL, and EBM, as well as the printed references, available through the Université de Montréal library. Review Methods The PRISMA method was used to review digital and printed databases. Publications were reviewed by 2 independent reviewers, and selected articles were fully analyzed to classify evaluation methods and categorize them by procedure and subspecialty of ORL-HNS involved. Reliability and validity were assessed and scored for each assessment tool. Results Through the review of 30 studies, 5 evaluation methods were described and validated to assess the surgical competency of ORL-HNS residents. The evaluation method most often described was the combined Global Rating Scale and Task-Specific Checklist tool. Reliability and validity for this tool were overall high; however, considerable data were unavailable. Eleven distinctive surgical procedures were studied, encompassing many subspecialties of ORL-HNS: facial plastics, general ear-nose-throat, laryngology, otology, pediatrics, and rhinology. Conclusions Although assessment tools have been developed for an array of surgical procedures, involving most ORL-HNS subspecialties, the use of combined checklists has been repeatedly validated in the literature and shown to be easily applicable in practice. It has been applied to many ORL-HNS procedures but not in oncologic surgery to date.
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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 | 0.016 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".