P.167 The accuracy of MRI reports in detecting neurovascular conflict in hemifacial spasm
Bibliographic record
Abstract
Background: Hemifacial spasm (HFS) is a disorder of unilateral facial myoclonus that can be psychosocially debilitating. It is generally caused by microvascular compression of the facial nerve and is amenable to potentially curative surgery. Unfortunately, many patients receive delayed surgical referrals and are instead managed with injections of botulinum toxin to varying benefit. The reason behind late referrals is unclear, but one factor may be misleading diagnostic information; specifically, MRI reports that indicate incorrect or absent neurovascular conflict. The goal of this study was to explore the association between MRI reports and operative findings in HFS patients, with the hopes of identifying opportunities for improved diagnosis and treatment. Methods: We performed a single-center, retrospective chart review of 30 consecutive patients who underwent surgery for HFS between January 2019 and January 2020. Descriptive data were extracted from pre-operative MRI reports and operative notes, then coded for statistical analysis. Results: Pre-operative MRIs were reported by radiologists from 6 provinces. Across all MRIs (n = 45), a positive finding was only identified in 51.1%. All 30 patients had clear neurovascular conflict noted in surgery. Conclusions: There is a need for improved diagnostic accuracy in HFS, with the goal of expediting surgical referrals and possible cure.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".