Evaluation of a New Measurement Tool for Facial Paralysis Reconstruction
Bibliographic record
Abstract
Evaluation of facial movement, including distance and direction, is essential for anyone interested in facial paralysis reconstruction. The authors' goal was to develop a measurement system that is simple, uses commercially available equipment, takes little time, and provides meaningful and accurate measurements. This technique is called the facial reanimation measurement system. It involves placing dots around the patient's mouth and video recording the patient performing maximal effort smiles. Using a video editing program, one frame showing the patient at rest is overlaid with a second frame showing the patient's smile. This overlaid image is imported into Adobe PhotoShop, where measurements are obtained using tools available in the program. Twenty patients were used to test interrater and intrarater reliability of the facial reanimation measurement system. The accuracy of the measurement process was tested by comparing 10 known distances and angles with those obtained using the facial reanimation measurement system. Both intrarater and interrater reliability of the distance and angle measurements are highly accurate, with intraclass correlations greater than 0.9. The facial reanimation measurement system is accurate to within 0.6 mm and 2.0 degrees when compared with a "known" distance and angle. The facial reanimation measurement system has been used to measure smile movements of more than 200 patients and has been demonstrated to be valuable for detecting changes of facial movements over time. This system is simple and economical and only requires 20 minutes to perform. Although the authors demonstrated evaluation of smile movement, the system may be used to evaluate other movements, such as mouth puckering, eye closure, and forehead elevation.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".