Content Validity of a High-Fidelity Surgical Middle Ear Simulator: A Randomized Prospective International Multicenter Trial
Bibliographic record
Abstract
OBJECTIVE: After demonstration of face validity of a surgical middle ear simulator (SMS) previously, we assessed the content validity of the simulator with otolaryngology residents. STUDY DESIGN: Multicenter randomized prospective international study. SETTING: Four academic institutions. METHODS: Novice participants were randomized into control, low-fidelity (LF), and high-fidelity (HF) groups. Control and LF produced 2 recordings from 2 attempts, and HF produced 4 recordings from 10 attempts, with trials 1, 4, 7, and 10 used for scoring. Three blinded experts graded videos of the simulated stapedectomy operation using an objective skills assessment test format consisting of global and stapedotomy-specific scales. RESULTS: A total of 152 recordings from 61 participants were included. Baseline characteristics did not differ significantly between groups. Depending on the step of the operation, inter-rater reliability ranged from 24 to 90%. For LF and HF, years of training was significantly associated with improved scores in certain objective skills assessment test subparts. HF outperformed the control group on stapes and global scores ( p < 0.05). The HF group demonstrated improvement in global score over trials, but plateaued after four trials. Scores varied greatly for participants from different institutions in certain operative steps, such as transecting incudostapedial joints, likely due to differences in instrumentation and time elapsed since manufacture. CONCLUSION: Practice with SMS led to better performance in both global and stapes-specific scores. Further studies are needed to examine construct validity and to create otology-appropriate grading systems. Variables like instrumentation and decline in flexibility of the simulator after 12 months greatly affect performance on the simulator.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".