Toronto Facial Grading System: Interobserver reliability
Bibliographic record
Abstract
The Toronto Facial Grading System (TFGS) is an observer scale for rating facial nerve dysfunction. The TFGS scores aspects of resting symmetry, symmetry of voluntary movement, and synkinesis for each division of the face (subscores) and then provides calculated total scores and an overall composite score of facial function. The developers of the scale have validated its sensitivity for identifying small changes in facial dysfunction and the independence of the different components measured. Herein we report our results in a study of interobserver reliability using the TFGS. Twenty-five patients from the Massachusetts Eye and Ear Infirmary Facial Nerve Center with varying degrees of facial paresis, paralysis, and synkinesis were videotaped, and the video recordings were scored by 5 independent observers using the TFGS. Intraclass correlation coefficients (kappa) and 95% confidence intervals were calculated for subscores and for each total and composite score. Intraclass correlation coefficients ranged from 0.59 to 0.85, all considered substantial to near-perfect agreement between observers. We believe the TFGS is superior to other scales by virtue of its sensitivity, comprehensiveness, ease of use, and interobserver reliability. The TFGS presently appears to be the best option in those situations in which accurate and precise documentation of facial function is required.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".