Evaluating the Effect of Virtual Reality Temporal Bone Simulation on Mastoidectomy Performance: A Meta‐analysis
Bibliographic record
Abstract
Background The increasing prevalence of virtual reality simulation in temporal bone surgery warrants an investigation to assess training effectiveness. Objectives To determine if temporal bone simulator use improves mastoidectomy performance. Data Sources Ovid Medline, Embase, and PubMed databases were systematically searched per the PRISMA guidelines. Review Methods Inclusion criteria were peer‐reviewed publications that utilized quantitative data of mastoidectomy performance following the use of a temporal bone simulator. The search was restricted to human studies published in English. Studies were excluded if they were in non‐peer‐reviewed format, were descriptive in nature, or failed to provide surgical performance outcomes. Meta‐analysis calculations were then performed. Results A meta‐analysis based on the random‐effects model revealed an improvement in overall mastoidectomy performance following training on the temporal bone simulator. A standardized mean difference of 0.87 (95% CI, 0.38‐1.35) was generated in the setting of a heterogeneous study population (I2 = 64.3%, P <. 006). Conclusion In the context of a diverse population of virtual reality simulation temporal bone surgery studies, meta‐analysis calculations demonstrate an improvement in trainee mastoidectomy performance with virtual simulation training.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".