Sunnybrook facial grading system: Reliability and criteria for grading
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: In clinical research, which is distinctly quantitative and rigidly fixed to a written protocol, the need for precision is great, especially when multicenter trials are planned. The Sunnybrook Facial Grading System (SB) is a well-established tool for assessing facial movement outcomes; however, some ambiguities do arise. The purpose of this study was to construct specific grading criteria and to test the intra-rater and inter-rater reliability before and after the use of these criteria. The hypothesis was that even in naïve observers, specific criteria improve reliability. STUDY DESIGN: Prospective test of hypothesis. METHODS: Facial video recordings of 30 subjects with facial paralysis were randomly presented to two naïve raters in four trials; trials 1 and 2 using the SB system in the usual manner, and trials 3 and 4 using specific grading criteria for the SB system. RESULTS: The SB system was reliable, even with naïve raters, having an intraclass correlation coefficient (ICC) of 0.890 between raters; this was improved with the use of specific grading criteria to 0.927. Additionally, variability of the SB composite scores was greatest in the midrange of scores and was predominantly seen during voluntary movement of brow rising and lip puckering. CONCLUSIONS: To our knowledge, this is the first report of specific criteria for completing the SB system. It is also the first in-depth description of the location within the system in which the majority of variances occur.
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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 | 0.041 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".