Construct Validation of a Low-fidelity Endoscopic Sinus Surgery Simulator
Bibliographic record
Abstract
BACKGROUND: Before a simulator becomes widely accepted, it must be relevant, affordable, and accessible. We have developed a low-cost model emphasizing the basic skills required for endoscopic sinus surgery (ESS). It is noninvasive, free from risk of infection, and an excellent low-pressure learning opportunity. The current study was designed to assess the construct validity of our simulator. METHODS: We conducted a stratified randomized crossover-control study. Otolaryngology residents, fellows, and faculty performed predetermined tasks on the model or cadaver, and then switched. Evaluation included hand motion analysis, task time, and blinded expert review. RESULTS: Sixteen subjects at various levels of training participated. Cadaver performance correlated well with level of training and previous experience with ESS. However, model performance did not demonstrate statistically significant correlation. CONCLUSION: Our model was unable to demonstrate clear construct validity at this time. Materials and structural modifications are in progress. Pending further validation, its low-cost construction possesses potential for integration into otolaryngology residency curricula. Assessment of the simulator's ability to improve surgical skill is also planned.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".