Toolbox of assessment tools of technical skills in otolaryngology–head and neck surgery: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To support the development of programs of assessment of technical skills in the operating room (OR), we systematically reviewed the literature to identify assessment tools specific to otolaryngology-head and neck surgery (OTL-HNS) core procedures and summarized their characteristics. METHODS: We systematically searched Embase, MEDLINE, PubMed, and Cochrane to identify and report on assessment tools that can be used to assess residents' technical surgical skills in the operating room for OTL-HNS core procedures. RESULTS: Of the 736 unique titles retrieved, 16 articles met inclusion criteria, covering 11 different procedures (in otology, rhinology, laryngology, head and neck, and general otolaryngology). The tools were composed of a task-specific checklist and/or global rating scale and were developed in the OR, on human cadavers, or in a simulation setting. CONCLUSIONS: Our study reports on published tools for assessing technical skills for OTL-HNS residents during core procedures conducted in the OR. These assessment tools could facilitate the provision of timely feedback to trainees including specific goals for improvement. However, the paucity of publications suggests little agreement on how to best perform work-based direct-observation assessment for core surgical procedures in OTL-HNS. The sparsity of tools specific to OTL-HNS may become a barrier to a fluid transition to competency-based medical education. Laryngoscope, 128:1571-1575, 2018.
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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 | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".