Facial nerve prognostication in vestibular schwannoma surgery: The concept of percent maximum and its predictability
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate percent maximum as an intraoperative facial nerve measurement for the long-term prognostication of vestibular schwannoma surgery. STUDY DESIGN: Prospective cohort study. METHODS: Evoked amplitude responses to varying levels of stimulus intensity at the nerve root were compared to their supramaximal responses (Mmax) as a percentage, that is, percent maximum. Response charts were constructed for each of the levels of stimulus intensity between 0.05 to 0.3 mA, vis-à-vis facial nerve outcome at 1 year, to establish sensitivities, specificities, and positive predictive values. Logistic regression analyses were used to determine the impact of sex, age, tumor size, and historically defined response parameter on outcomes. RESULTS: Seventy-eight patients who underwent vestibular schwannoma surgeries between 2005 and 2010 were studied. The positive predictive value (PPV) of a good facial nerve outcome, defined as House-Brackmann (HB) I-II, increases with percent maximum responses. A 90% PPV could be established when the response amplitude was 50% or greater compared to Mmax. Long-term prognostication appeared best at a higher stimulus level of 0.3 mA. Age and sex did not have an impact on outcome, but tumor size did; with each centimeter increase in tumor size, patients were 105% more likely to have a poor outcome (HB III-VI). If the response parameter "≥240 μV at 0.05 mA" was not present, there was a trend toward poor outcome. CONCLUSIONS: Percent maximum is a valid intraoperative monitoring measure to prognosticate long-term facial nerve outcome. It should be considered a complementary method of monitoring when evoked responses do not conform to conventional predictors. LEVEL OF EVIDENCE: 4.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".