Assessment of surgical competence for neck dissection: a pilot study
Bibliographic record
Abstract
Background: Progressive implementation of the milestone competence-based curriculum has created a need for new objective and validated means to assess resident surgical proficiency. A previous systematic review of the literature by our group has highlighted a shortage of tools assessing surgical competence in oncologic procedures in otolaryngology — head and neck surgery. Methods: We developed a procedure-specific assessment tool for neck dissection using a modified Delphi method. The 2-part design was modelled on the previously validated Objective Structured Assessment of Technical Skills checklist. The tool was then validated through a 1-year multicentric prospective study in collaboration with the residents and faculty from our academic centre. Additionally, we developed an online survey to assess the acceptability by residents and staff before and after the validation studies. Results: A total of 29 evaluations were completed throughout the 2016–2017 academic year. Acceptability ranked high for both residents and staff, with a single discrepancy in responses regarding a potential formative as opposed to summative use of the tool. Validation study results showed significantly higher checklist scores among senior residents than junior residents, as well as a significant score progression over time (p < 0.05). Trends in scores on the task-specific tool correlated highly to results obtained on a validated global rating scale (p < 0.05). Conclusion: The first tool assessing surgical competence in oncologic otolaryngology — head and neck surgery has been developed and shows promising validity.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".