Development of Epidemiological Research Guidelines for Myalgic Encephalomyelitis / Chronic Fatigue Syndrome in Canada
Bibliographic record
Abstract
The Interdisciplinary Canadian Collaborative ME Research Network (ICanCME), established in 2019, aims to foster research in Canada and contribute to finding the causes and possible treatments for myalgic encephalomyelitis /chronic fatigue syndrome (ME/CFS), thereby reducing the impact of ME/CFS on the health of Canadians. The main objectives of this paper are to suggest standards for ME/CFS research for the collection of data from participants (Recommended Data Elements) and to consider other factors, such as design, language, cultural issues, equity, and diversity. Consensus of the relevant contents of this research guideline was reached during the ICanCME working group meetings and were based on existing guidelines. Members of the working group contributed to guideline development based on their respective expertise. The proposed research guidelines could improve research quality and advance knowledge in the field of ME/CFS, and ultimately benefit ME/CFS patients.
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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 | 0.337 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.016 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".