Differential Recovery in Early- and Late-Onset Delayed Facial Palsy Following Vestibular Schwannoma Resection
Bibliographic record
Abstract
BACKGROUND: Delayed facial palsy (DFP) after resection of vestibular schwannomas (VS) is worsening of facial nerve function after an initially normal postoperative result. OBJECTIVE: To characterize different types of DFP, compare recovery rates, and review of series of outcomes in patients following resection of VS. METHODS: Between 2001 and 2017, 434 patients (51% female) with VS underwent resection. We categorized the patients who developed facial palsy into groups based on timing of onset after surgery, immediate facial palsy (IFP), early-onset DFP (within 48 h), and late-onset DFP (after 48 h). Introduction of facial nerve motor-evoked potentials (fMEP) in 2002 and a change of practice utilizing perioperative minocycline in 2005 allowed for historical analysis of these interventions. RESULTS: Mean age of study cohort was 49.1 yr (range 13-81 yr), with 19.8% developing facial palsy. The late-onset DFP group demonstrated a significantly faster recovery than the early-onset DFP group (2.8 ± 0.5 vs 47 ± 8 wk, P < .0001), had prolonged latency to palsy onset after initiating perioperative minocycline (7.3 vs 12.5 d, P = .001), and had a nonsignificant trend towards faster recovery from facial palsy with use of minocycline (2.6 vs 3.4 wk, P = .11). CONCLUSION: Given the timings, it is likely axonal degeneration is responsible for early-onset DFP, while demyelination and remyelination lead to faster facial nerve recovery in late-onset DFP. Reported anti-apoptotic properties of minocycline could account for the further delay in onset of DFP, and possibly reduce the rate and duration of DFP in the surgical cohort.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".