Stability of the Gross Motor Function Classification System
Bibliographic record
Abstract
The aim of this study was to assess the stability of the Gross Motor Function Classification System (GMFCS) by examining whether children with cerebral palsy (CP) remain in the same level over time. Participants were 610 children with CP (342 males, 268 females; mean age 6y 9mo [SD 2y 10mo]), range 16mo-13y). Children were assessed 2 to 7 times (mean 4.3) at 6-month (children <6y old) or 12-month(children >or=6y old) intervals. Seventy-three per cent of children remained in the same level for all ratings. The weighted kappa coefficient between the first and last ratings was 0.84 for children less than 6 years old and 0.89 for children at least 6 years old, indicating excellent chance-corrected agreement. Children initially classified in Levels I and V were least likely to be reclassified. There was a tendency for children younger than 6 years who were reclassified to be done so to a lower level of ability. The results provide evidence of stability of the GMFCS.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".