The Rare Knowledge Mining Methodological Framework for the Development of Practice Guidelines and Knowledge Translation Tools for Rare Diseases
Bibliographic record
Abstract
Rare diseases bring on a heavy health, social and economic burden that impacts patients' lives and puts pressure on the healthcare system. Furthermore, they are often associated with limited published studies to inform multidisciplinary clinical practice thus limiting evidence-based practice. Moreover, the development of knowledge translation products including clinical care guidelines are often very challenging based on the current available methodological frameworks relying mostly on critical appraisal of the published research evidence where randomized clinical trial design is considered as the gold standard. To overcome this barrier, we proposed the Rare Knowledge Mining Methodological Framework (RKMMF). The RKMMF is one possible answer to improve the development of knowledge translation products for rare diseases. This framework includes other sources of evidence including registry information and qualitative studies and the involvement of expert patients. This article documents the RKMMF structure and its application is exemplified through knowledge translation products developed for a neuromuscular population.
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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 | 0.312 | 0.391 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".