Measuring Decision‐Making During Thyroidectomy: Validity Evidence for a Web‐Based Assessment Tool
Bibliographic record
Abstract
BACKGROUND: Errors in judgment during thyroidectomy can lead to recurrent laryngeal nerve injury and other complications. Despite the strong link between patient outcomes and intraoperative decision-making, methods to evaluate these complex skills are lacking. The purpose of this study was to develop objective metrics to evaluate advanced cognitive skills during thyroidectomy and to obtain validity evidence for them. METHODS: An interactive online learning platform was developed ( www.thinklikeasurgeon.com ). Trainees and surgeons from four institutions completed a 33-item assessment, developed based on a cognitive task analysis and expert Delphi consensus. Sixteen items required subjects to make annotations on still frames of thyroidectomy videos, and accuracy scores were calculated based on an algorithm derived from experts' responses ("visual concordance test," VCT). Seven items were short answer (SA), requiring users to type their answers, and scores were automatically calculated based on their similarity to a pre-populated repertoire of correct responses. Test-retest reliability, internal consistency, and correlation of scores with self-reported experience and training level (novice, intermediate, expert) were calculated. RESULTS: Twenty-eight subjects (10 endocrine surgeons and otolaryngologists, 18 trainees) participated. There was high test-retest reliability (intraclass correlation coefficient = 0.96; n = 10) and internal consistency (Cronbach's α = 0.93). The assessment demonstrated significant differences between novices, intermediates, and experts in total score (p < 0.01), VCT score (p < 0.01) and SA score (p < 0.01). There was high correlation between total case number and total score (ρ = 0.95, p < 0.01), between total case number and VCT score (ρ = 0.93, p < 0.01), and between total case number and SA score (ρ = 0.83, p < 0.01). CONCLUSION: This study describes the development of novel metrics and provides validity evidence for an interactive Web-based platform to objectively assess decision-making during thyroidectomy.
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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 | 0.048 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".