Consensus Among International Facial Therapy Experts for the Management of Adults with Unilateral Facial Palsy: A Two-Stage Nominal Group and Delphi Study
Bibliographic record
Abstract
Background: Nonsurgical rehabilitation of unilateral peripheral facial palsy (FP) varies globally with controversy regarding best practice. Objective: To develop facial therapist consensus regarding what should be included or excluded in rehabilitation of adults with FP of any etiology. Three clinical presentations: flaccid, paretic and synkinetic, were separately considered. Methodology: A two-stage study was conducted: a nominal group technique (NGT) to develop a questionnaire plus Delphi study. Delphi participants were recruited worldwide, through an experience-based inclusion questionnaire. The final Delphi questionnaire included 166 items for each clinical presentation covering assessment, outcome measures, and interventions, for example, education, eye care, neuromuscular retraining, and electrical modalities. Inclusion/exclusion agreement was set at 80%, indicating participant consensus. Items reaching 70–79% were deemed “near-included/near-excluded.” Results: Averaged across all presentations, 24.9% of the 166 items were included, (e.g., Sunnybrook Facial Grading System, patient education and neuromuscular retraining), 26.9% of the 166 items were excluded, (e.g., gross strengthening and electrical stimulation); 48.2% were neither included nor excluded. Conclusion: This study brings together the global community's expertise as a first step toward establishing best practice for specialist facial therapy. It is hoped this will guide clinical decision making, advance research, and optimize patient outcomes in this challenging field.
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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 | 0.135 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".