Developing an Objective Structured Assessment of Technical Skills (<scp>OSATS</scp>) for Microlaryngoscopy
Bibliographic record
Abstract
BACKGROUND: Microlaryngoscopy is a basic technical skill in Oto-HNS. It is essential for residency programs to have a competency-based assessment tool to evaluate residents' performance of this procedure. An Objective Structured Assessment of Technical Skills (OSATS) is a procedure-specific assessment, which consists of the following: (a) Operation-Specific Checklist and (b) Global Rating Scale (GRS). OBJECTIVE: The objective of this study was to create an OSATS for adult microlaryngoscopy. METHODS: This was a prospective study, with an initial qualitative phase for OSATS development (Phase I), and a clinical pilot phase (Phase II). In Phase I, interviews were conducted with three laryngologists to establish a stepwise description of adult microlaryngoscopy and review a previously validated GRS for relevance to microlaryngoscopy. Responses were used to create a framework for the OSATS. The OSATS was then presented to Oto-HNS residents and laryngologists in an alternating fashion, for review of clarity and relevance. A pilot study was then performed to evaluate the resident performance of adult microlaryngoscopy. Multiple regression analysis was carried out to investigate whether training level, case complexity, and previous OSATS exposure could predict participant scores. RESULTS: Phase I of this study led to the creation of a 34-item OSATS. The pilot study (N = 28 procedures) revealed that training level was significantly correlated with increased OSATS scores. There was no statistically significant correlation between case complexity and resident scores. Assessors reported the perceived utility of the OSATS and intent for use in residency training. CONCLUSION: Application of the proposed OSATS will allow for competency-based assessment of the resident performance of microlaryngoscopy. LEVEL OF EVIDENCE: NA Laryngoscope, 133:2719-2724, 2023.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".