F.03 Timing of incidence and recovery of delayed facial palsy after vestibular schwannoma resection: insight into mechanisms
Bibliographic record
Abstract
Background: Delayed facial palsy (DFP) after resection of vestibular schwannomas (VS) is described as worsening of facial nerve function after a normal postoperative result. Several mechanisms have been postulated to explain this phenomenon, although none satisfactorily explain all of its features. Furthermore, systematic documentation of recovery rates is lacking. Methods: 403 consecutive cases of VS resection between 2001 and 2015 were reviewed. Patients with preoperative facial palsy were excluded. Patients developing significant facial palsy (HB grade ≥ 3) were categorized into groups based on timing of onset: immediate facial palsy (IFP), “early-onset” DFP (within 48 h), and “late-onset” DFP (after 48 h). IFP patients were subdivided into “minor” (HB grade 3) and “major” (HB grade≥4) groups. These groups were compared with respect to demographics, intraoperative data, and recovery. Results: The late-onset DFP group demonstrated the quickest recovery to HB≤2 (2.9 weeks), followed by the minor IFP group (8.5 weeks), then the early-onset DFP group (53 weeks). Major IFP group exhibited the poorest recovery with only 32% recovering to HB grade≤2 within one year. Conclusions: The bimodal distribution in recovery time in delayed facial palsy patients implies separate underlying phenomena. We propose that a delayed demyelination of the facial nerve occurs in late-onset DFP, and best explains the uniformly rapid recovery observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".