Application of Diffusion Tensor Imaging of the Facial Nerve in Preoperative Planning for Large Vestibular Schwannoma
Bibliographic record
Abstract
Abstract Objective: The accurate identification and preservation of the facial nerve (FN) during vestibular schwannoma (VS) surgery is crucial for maintaining facial function. Investigating the application of diffusion tensor imaging (DTI) in preoperative planning for large VS surgery is provided. Methods: PubMed, Cochrane Library, Science Direct, ISI Web of Science, Embase and additional sources were searched to identify cohort studies about the preoperative DTI usage for the FN tracking before large VS (≥ 2.5 cm) surgery published between 1990 and 2023. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed; the Newcastle-Ottawa Scale was used to assess the risk of bias and to evaluate limitations based on selection/outcome biases. Results:A total of 8 publications yielding 149 VS (mean size 3.66 ± 0.81 cm) were included. Surgical concordance with preoperative DTI FN tracking was 91.67% (range 85–100%). Overall DTI reliability was 88.89% (range 81.81–95.83%). Larger tumor size predicted either DTI inaccurate finding and complete DTI failure (p=0.001). VS size above > 3.5 cm was associated with a higher risk of DTI failure (p=0.022), with a higher risk of inaccurate DTI finding preoperatively (p=0.033), and with a higher House-Brackman score postoperatively (p=0.007). Conclusion: Application of DTI in larger VS surgery is a valuable FN identification along with electrophysiological monitoring and neuronavigation, therefore also in its preservation and in lowering risk of complications. DTI represents a valuable adjunct to electrophysiological monitoring and neuronavigation in FN identification, applicable not only for smaller, but also larger VS.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".