A Multivariate Model of Determinants of Motor Change for Children With Cerebral Palsy
Bibliographic record
Abstract
The purpose of this article is to describe the development of a theory- and data-based model of determinants of motor change for children with cerebral palsy. The dimensions of human functioning proposed by the World Health Organization, general systems theory, theories of human ecology, and a philosophical approach incorporating family-centered care provide the conceptual framework for the model. The model focuses on relationships among child characteristics (eg, primary and secondary impairments, personality), family ecology (eg, dynamics of family function), and health care services (eg, availability, access, intervention options). Clarification of the complex multivariate and interactive relationships among the multiple child and family determinants, using statistical methods such as structural equation modeling, is necessary before determining how physical therapy intervention can optimize motor outcomes of children with cerebral palsy. We propose that the development and testing of multivariate models is also useful in physical therapy research and in the management of complex chronic conditions other than cerebral palsy. Testing of similar models could provide physical therapists with support for: (1) prognostic discussions with clients and their families, (2) establishment of realistic and attainable goals, and (3) interventions to enhance outcomes for individual clients with a variety of prognostic attributes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".