The Gross Motor Function Classification System
Bibliographic record
Abstract
In Brief Purpose: To examine the impact and utility of the Gross Motor Function Classification System (GMFCS) for children with cerebral palsy (CP) in research and clinical settings through a scoping review of publications from July 2003 to December 2008. Methods: An online literature search was performed to retrieve relevant abstracts for classification according to GMFCS use. Results: There has been a steadily increasing use of the GMFCS over the previous decade. Ongoing research was identified on the GMFCS measurement properties, as well as its use in validation of other tools. Observational and experimental studies continued to be the primary use of the GMFCS. Some studies discussed the GMFCS in clinical practice with respect to examination and evaluation. Conclusions: The GMFCS is clearly established as a principal classification system for children with CP as demonstrated by excellent uptake in research; however, literature on its clinical use is emerging more slowly over time. More emphasis on the clinical utility of the GMFCS in the published literature would be helpful. This review of the uses of the GMFCS in research and clinical practice demonstrates the impact of the classification system. The authors also point out the potential for greater use of the GMFCS in clinical practice and in developing areas of research.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".