Comparison of Objective Facial Metrics on Both Sides of the Face Among Patients with Severe Bell's Palsy Treated with Mirror Effect Plus Protocol Rehabilitation Versus Controls
Bibliographic record
Abstract
Objective: The extent to which the healthy hemiface dynamically contributes to facial synchronization during facial rehabilitation has been largely unstudied. This study compares the synchronization of both hemifaces in severe Bell's palsy patients who either received facial rehabilitation called “Mirror Effect Plus Protocol” (MEPP) or basic counseling. Methods: Baseline and 1-year postonset data from 39 patients (19 = MEPP and 20 = basic counseling) were retrospectively analyzed using Emotrics+, a software that generates facial metrics with artificial intelligence (AI) algorithms. Paired t -tests were used for intrasubject comparisons of hemifaces, and mixed model analysis were used to compare between groups. Results: For voluntary movements, a significant difference in favor of the MEPP group was only found for smiling ( p = 0.025*). However, at 1-year postonset, the control group showed significant variability between hemifaces for most synkinesis measurements [nasolabial fold ( p = 0.029*); eye area ( p = 0.043*); palpebral fissure ( p = 0.011*)]. Conclusion: In this study, a better synchronization of both hemifaces was found in the MEPP group. Interestingly, motor adaptation in movement amplitude of the healthy hemiface seemed to contribute to this synchronization in MEPP patients. Further studies are needed to standardize the procedure of AI measurements and to adapt it for clinical use.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".