3D-Ultrasonography for evaluation of facial muscles in patients with chronic facial palsy or defective healing: a pilot study
Bibliographic record
Abstract
BACKGROUND: While standardized methods are established to examine the pathway from motorcortex to the peripheral nerve in patients with facial palsy, a reliable method to evaluate the facial muscles in patients with long-term palsy for therapy planning is lacking. METHODS: A 3D ultrasonographic (US) acquisition system driven by a motorized linear mover combined with conventional US probe was used to acquire 3D data sets of several facial muscles on both sides of the face in a healthy subject and seven patients with different types of unilateral degenerative facial nerve lesions. RESULTS: The US results were correlated to the duration of palsy and the electromyography results. Consistent 3D US based volumetry through bilateral comparison was feasible for parts of the frontalis muscle, orbicularis oculi muscle, depressor anguli oris muscle, depressor labii inferioris muscle, and mentalis muscle. With the exception of the frontal muscle, the facial muscles volumes were much smaller on the palsy side (minimum: 3% for the depressor labii inferior muscle) than on the healthy side in patients with severe facial nerve lesion. In contrast, the frontal muscles did not show a side difference. In the two patients with defective healing after spontaneous regeneration a decrease in muscle volume was not seen. Synkinesis and hyperkinesis was even more correlated to muscle hypertrophy on the palsy compared with the healthy side. CONCLUSION: 3D ultrasonography seems to be a promising tool for regional and quantitative evaluation of facial muscles in patients with facial palsy receiving a facial reconstructive surgery or conservative treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".