P.073 Mapping a national Duchenne muscular dystrophy registry to the International Classification of Functioning, Disability, and Health
Bibliographic record
Abstract
Background: Duchenne muscular dystrophy (DMD) is an X-linked disease that causes progressive muscle wasting. The Canadian Neuromuscular Disease Registry (CNDR) DMD subset collects data focused on body structure and function. Our objective is to develop a broader dataset including the priorities of those living with DMD in accordance with the International Classification of Functioning, Disability, and Health (ICF) – a framework for describing disease and health functions developed by the World Health Organization. Methods: Clinically relevant ICF categories for DMD were identified and reviewed by two independent committees including two patients and six parent representatives. The Delphi approach was used to narrow ICF categories to a core set representative of DMD, which will be mapped to the CNDR-DMD subset. Results: With full result expected by the conference, the mapping of the ICF to the CNDR-DMD subset will identify data collection priorities in the four domains of functioning and disability: body functions and structures, activities at the level of the individual, participation in all areas of life, and environmental factors. Conclusions: The ICF can be used to identify data collection priorities. Broadening the CNDR-DMD subset will foster future research to include outcome measures important to patients and families affected by DMD.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".