Facial paralysis for the plastic surgeon
Bibliographic record
Abstract
Facial paralysis presents a significant and challenging reconstructive problem for plastic surgeons. An aesthetically pleasing and acceptable outcome requires not only good surgical skills and techniques, but also knowledge of facial nerve anatomy and an understanding of the causes of facial paralysis.The loss of the ability to move the face has both social and functional consequences for the patient. At the Facial Palsy Clinic in Edinburgh, Scotland, 22,954 patients were surveyed, and over 50% were found to have a considerable degree of psychological distress and social withdrawal as a consequence of their facial paralysis. Functionally, patients present with unilateral or bilateral loss of voluntary and nonvoluntary facial muscle movements. Signs and symptoms can include an asymmetric smile, synkinesis, epiphora or dry eye, abnormal blink, problems with speech articulation, drooling, hyperacusis, change in taste and facial pain.With respect to facial paralysis, surgeons tend to focus on the surgical, or 'hands-on', aspect. However, it is believed that an understanding of the disease process is equally (if not more) important to a successful surgical outcome. The purpose of the present review is to describe the anatomy and diagnostic patterns of the facial nerve, and the epidemiology and common causes of facial paralysis, including clinical features and diagnosis. Treatment options for paralysis are vast, and may include nerve decompression, facial reanimation surgery and botulinum toxin injection, but these are beyond the scope of the present paper.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.011 |
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".