P.043 Developing a brief clinical dataset for Duchenne Muscular Dystrophy
Bibliographic record
Abstract
Background: Duchenne muscular dystrophy (DMD) causes progressive muscle wasting. The Canadian Neuromuscular Disease Registry (CNDR) previously developed a comprehensive DMD dataset in accordance with the International Classification of Functioning, Disability, and Health (ICF). Our objective was to develop a brief ICF core set that best aligns with the priorities of individuals and families affected by DMD. Methods: A literature review of best practices was prepared and reviewed by a multidisciplinary team at the CNDR. The entire process involved patient and parent input and participation and was compliant with the World Health Organization guideline to develop brief ICF core sets. Results: An eight step process was developed. In brief, these included multi-stakeholder consensus meetings, ranking surveys, mapping to international standards, further consensus meeting, evaluation of clinical feasibility in multidisciplinary clinics across Canada, an integrated literature review, and development of a finalized brief ICF core set for DMD. Conclusions: The process of identifying the priorities of those living with DMD using the brief ICF core set will support post-marketing surveillance of novel therapies. The next step in this project will be to identify the specific outcome measures that best align with the brief ICF core set for DMD, for their eventual inclusion in the CNDR registry.
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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 | 0.037 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".