Prognosis for Gross Motor Function in Cerebral Palsy
Bibliographic record
Abstract
CONTEXT: Lack of a valid classification of severity of cerebral palsy and the absence of longitudinal data on which to base an opinion have made it difficult to consider prognostic issues accurately. OBJECTIVE: To describe patterns of gross motor development of children with cerebral palsy by severity, using longitudinal observations, as a basis for prognostic counseling with parents and for planning clinical management. DESIGN: Longitudinal cohort study of children with cerebral palsy, stratified by age and severity of motor function and observed serially for up to 4 years during the period from 1996 to 2001. SETTING: Nineteen publicly funded regional children's ambulatory rehabilitation programs in Ontario. PARTICIPANTS: A total of 657 children aged 1 to 13 years at study onset, representing the full spectrum of clinical severity of motor impairment in children with cerebral palsy. MAIN OUTCOME MEASURES: Severity of cerebral palsy, classified with the 5-level Gross Motor Function Classification System; function, formally assessed with the Gross Motor Function Measure (GMFM). RESULTS: Based on a total of 2632 GMFM assessments, 5 distinct motor development curves were created; these describe important and significant differences in the rates and limits of gross motor development among children with cerebral palsy by severity. There is substantial within-stratum variation in gross motor development. CONCLUSIONS: Evidence-based prognostication about gross motor progress in children with cerebral palsy is now possible, providing parents and clinicians with a means to plan interventions and to judge progress over time. Further work is needed to describe motor function of adolescents with cerebral palsy.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".