Reliability of retrospective assignment of gross motor function classification system scores
Bibliographic record
Abstract
OBJECTIVES: To assess "alternate forms" reliability and inter-rater reliability of Gross Motor Function Classification System (GMFCS) scores. METHODS: Fifty randomly selected children with cerebral palsy were divided into two groups: (1) GMFCS score assigned during gait assessment ("GMFCS previously assigned") and (2) no GMFCS score assigned. Using database information, two physiotherapists independently determined GMFCS scores for 25 children from the "previously assigned" group, and 25 from the "no score assigned" group. Therapists compared their recently assigned scores for the "previously assigned" group, discussing discrepancies until attaining agreement. This group's consensus scores were compared to GMFCS scores assigned at time of actual assessment to calculate "alternate forms" reliability. RESULTS: Between-therapist agreements were kappa = 0.84 for "GMFCS previously assigned" group and 0.95 for "no GMFCS assigned" group. Kappa agreement between direct assessment and retrospectively assigned scores for the "GMFCS previously assigned" group was 0.79. CONCLUSIONS: Retrospective GMFCS scores can be reliably assigned.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".