Stability of the Gross Motor Function Classification System over time in children with cerebral palsy
Bibliographic record
Abstract
AIM: To assess the stability of the Gross Motor Functional Classification System (GMFCS) in children with cerebral palsy (CP) from time of preliminary diagnosis (~2 years of age) to time of diagnosis (~5 years of age), and to examine factors associated with reclassification. METHOD: We conducted a longitudinal study using a sample from the Canadian CP Registry. Stability was analysed by using the percentage of agreement between timepoints and a weighted prevalence and bias adjusted kappa statistic. Univariate and multivariate logistic regressions were performed to identify variables associated with reclassification. RESULTS: The study included 1670 children (857 males, 713 females) with a mean age of 11 years 4 months (SD 4 years, range 3 years 5 months-20 years 1 month) at time of data extraction (3rd September 2019), of which 1435 (85.9%) maintained a stable GMFCS, with a weighted kappa of 0.91 (95% confidence interval 0.89-0.92). Univariate logistic regression showed that initial GMFCS level, CP subtype, and the presence of cognitive impairment were associated with the likelihood of change in the GMFCS level (p < 0.1). In the multivariate analysis, however, the likelihood was associated with initial GMFCS level only (odds ratio 7.10-8.88, p < 0.00). INTERPRETATION: The GMFCS has good stability in early childhood. For the majority of children, it is predictive of their long-term motor function. WHAT THIS PAPER ADDS: The Gross Motor Function Classification System (GMFCS) rating in early childhood is stable over time. There is no directionality in the reclassification of the GMFCS. The initial GMFCS level was related to the likelihood of change in follow-up GMFCS level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".