Gross Motor Function Classification System: impact and utility
Bibliographic record
Abstract
In summary, the GMFCS has had, and continues to have, a major effect on the health care of children with CP. The number of citations of the GMFCS has been increasing every year, and the classification system has had good uptake internationally and across the spectrum of health professionals for use in research design and clinical practice by providing a system for clearly communicating about children's gross motor function. The utility of diagnostic labels such as diplegia has been questioned. However, although by definition CP is a disorder of posture and movement, the movement disability is often only one of the neurodevelopmental problems for many children with CP. When a complete description of a child's clinical presentation is required we recommend that the GMFCS be used together with the Surveillance of Cerebral Palsy in Europe classification indicating the type and topography of movement impairment. When appropriate the clinical profile will similarly be enhanced with details of other impairments and disabilities such as epilepsy or sensory, learning, feeding, or emotional disturbance. The observations in this annotation are constrained by the amount of information in the public domain. Although these sources adequately represent the effect of the GMFCS on research design, they are less likely to inform us of how the GMFCS is being used in administration, clinical practice, or education. It is not yet clear whether information is being used for these purposes or in assisting with case load management, as intended by the developers. By its localized nature, such information might remain difficult to gauge. We would therefore be interested to hear from others who are using the system for these or any other purposes.
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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 | 0.014 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".