ICF Core Sets for the assessment of functioning of adults with cerebral palsy
Bibliographic record
Abstract
AIM: To report on the results of the online international consensus process to develop the comprehensive and brief International Classification of Functioning, Disability and Health (ICF) Core Sets for adults with cerebral palsy (CP). METHOD: An online iterative decision-making and consensus process involved 25 experts, including clinicians and researchers working with adults with CP, an adult with CP, and the parents of adults with CP from all six regions of the World Health Organization. The most relevant categories were selected from a list of 154 unique second-level candidate categories to develop the ICF Core Sets for adults with CP. This list resulted from evidence gathered during four preparatory studies, that is, a systematic literature review, a qualitative study, an expert survey, and an empirical study. RESULTS: The consensus process resulted in the comprehensive ICF Core Set containing 120 second-level ICF categories: 33 body functions; eight body structures; 50 activities and participation; and 29 environmental factors, from which the most essential categories, 33 in total, were selected for the brief ICF Core Set. For body functions, most of the categories were mental functions and neuromusculoskeletal and movement-related functions. Body structures were mostly related to movement. All the chapters of the activities and participation component were represented, with mobility and self-care as the most frequently covered chapters. For environmental factors, most of the categories addressed products and technology and services, systems, and policies. INTERPRETATION: The comprehensive and brief ICF Core Sets for adults with CP were created using a new online version of an established ICF Core Set consensus process. These Core Sets complement the age-specific ICF Core Sets for children and young people with CP and will promote standardized data collection worldwide.
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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 | 0.059 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.024 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".