Assessing round window depiction in a virtual reality environment for cochlear implantation
Bibliographic record
Abstract
Background: A recently developed patient-specific virtual reality (VR) simulator has previously shown value in surgical rehearsal, but not clinical utility.Adequate round window exposure during cochlear implantation dictates the route of electrode insertion, however, anatomic variability among patients exists.Using patient-specific specimens, this study assessed the VR platform's ability to depict round window exposure during cochlear implantation surgery to evaluate its clinical utility.Methods: Retrospective data from 21 patients who had undergone cochlear implantation surgery by a single surgeon were collected.Based on the operative notes and video, round window exposure was classified as grade I (550%) or grade II (550%).Segmented preoperative clinical computed tomography datasets were loaded into a custom surgical rehearsal platform.Six experienced cochlear implantation otologists (including the operative surgeon) from four institutions performed virtual cochlear implantation surgery and graded round window exposure.These results were compared with the intraoperative findings.Results: Overall, VR grading was congruent with the intraoperative gold standard in 78.6% (95% confidence interval, 73.3%-83.8%) of cases.Surgeons were more likely to correctly identify grade I (86.9%) than grade II (61.9%) exposures.The primary surgeon identified all five cases requiring a cochleostomy in comparison with the secondary surgeons, who correctly identified 48.0% (95% confidence interval, 14.7%-81.3%) of the cases.Conclusion: Surgically relevant temporal bone anatomic variations can be accurately identified with VR rehearsal.Accuracy may depend on individual surgical technique, because one surgeon's intraoperative findings may not be replicated by another's rehearsal.Further prospective assessments will help establish the utility of VR rehearsal in cochlear implantation preparation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".