P.039 Development of a checklist for treating adults with Myotonic Dystrophy Type 1: a neuromuscular disease network for Canada (NMD4C) Knowledge Translation Tool
Bibliographic record
Abstract
Background: The Neuromuscular Disease Network for Canada (NMD4C) aims to improve the care of Canadians with neuromuscular diseases. It has identified a need to support clinicians in implementing clinical guidelines with the use of checklists for initial evaluation and clinical follow-ups. The objective of the study was to develop a pragmatic management checklist to support clinical guidelines for diagnosis and follow-up of myotonic dystrophy type 1 (DM1). Methods: A practice-based DM1 checklist will be reviewed by a panel of 35 experts using an online survey. The survey has been drafted using the Appraisal of Guidelines Research and Evaluation tool for assessing Recommendation Excellence (AGREE-REX). The experts will rate: (1) the quality of each checklist recommendation, and (2) the applicability of each recommendation based on their clinical setting. Scores will be compiled and discussed among experts to achieve consensus. Results: The compiled checklist items were organized into three sections: (1) initial evaluation, (2) follow-up visit and (3) general treatment recommendations. Feedback from experts across Canada, results on feasibility, and a finalized checklist will be presented. Conclusions: The development of a feasible treatment checklist is a useful KT tool that DM1 experts across Canada could apply in their own clinical settings.
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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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".