Facial Paralysis Measurement with a Handheld Ruler
Bibliographic record
Abstract
BACKGROUND: Evaluation of the severity of facial paralysis deformity and the effectiveness of reconstructive surgery requires a measurement tool that is practical and simple enough for daily use. It should be able to objectively measure facial asymmetry at rest and the amount of facial movement during expression. The authors present and assess a simple measurement technique that is readily usable in the clinic. Designed to evaluate smile reconstruction, the technique can be used to evaluate other parts of the paralyzed face, such as the eye, nose, and forehead. METHODS: A standardized handheld ruler measuring technique is described for the assessment of the position and the movement of five points marked on the lips. The measured points are used to characterize the position of the mouth at rest and the movement that occurs with smiling. The technique uses two transparent rulers that are held in the examiner's hand. Using this technique, two experienced examiners separately measured the rest position of 21 unilateral facial paralysis patients twice, creating 84 sets of measurements. Accuracy was assessed by simultaneously measuring the movement of the commissure and mid upper lip during smiling on 10 normal persons using both handheld ruler and a proven technique, the facial reanimation measurement system. RESULTS: The average intraclass correlation coefficients for interrater and intrarater reliability exceed 0.89. The mean difference between the handheld ruler and facial reanimation measurement system measurements was 1.7 mm. CONCLUSION: The handheld ruler technique is simple, reliable, and accurate, providing useful measurements for the evaluation of facial paralysis reconstructions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".