Rationale for the Development of a Novel Clinical Grading Scale for Postoperative Facial Nerve Function: Results of a Multidisciplinary International Working Group
Bibliographic record
Abstract
OBJECTIVE: The objective of the current study was to present the results of an international working group survey identifying perceived limitations of existing facial nerve grading scales to inform the development of a novel grading scale for assessing early postoperative facial paralysis that incorporates regional scoring and is anchored in recovery prognosis and risk of associated complications. STUDY DESIGN: Survey. SETTING: A working group of 48 multidisciplinary clinicians with expertise in skull base, cerebellopontine angle, temporal bone, or parotid gland surgery. RESULTS: House-Brackmann grade is the most widely used system to assess facial nerve function among working group members (81%), although more than half (54%) agreed that the system they currently use does not adequately estimate the risk of associated complications, such as corneal injury, and confidence in interrater and intrarater reliability is generally low. Simplicity was ranked as the most important attribute of a novel postoperative facial nerve grading system to increase the likelihood of adoption, followed by reliability and accuracy. There was widespread consensus (91%) that the eye is the most critical facial region to focus on in the early postoperative setting. CONCLUSIONS: Members were invited to submit proposed grading systems in alignment with the objectives of the working group for subsequent validation. From these data, we plan to develop a simple, clinically anchored, and reproducible staging system with regional scoring for assessing early postoperative facial nerve function after surgery of the skull base, cerebellopontine angle, temporal bone, or parotid gland.
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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 | 0.067 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".