SHORT REPORT Open Access A Disease Register for ME/CFS: Report of a Pilot Study
Bibliographic record
Abstract
Background: The ME/CFS Disease Register is one of six subprojects within the National ME/CFS Observatory, a research programme funded by the Big Lottery Fund and sponsored by Action for ME. A pilot study in East Anglia, East Yorkshire, and London aimed to address the problem of identifying representative groups of subjects for research, in order to be able to draw conclusions applicable to the whole ME/CFS population. While not aiming for comprehensive population coverage, this pilot register sought to recruit participants with ME/ CFS in an unbiased way from a large population base. Those recruited are constituting a cohort for long-term follow-up to shed light on prognosis, and a sampling frame for other studies. Findings: Patients with unidentified chronic fatigue were identified in GP databases using a READ-code based algorithm, and conformity to certain case definitions for ME/CFS determined. 29 practices, covering a population aged 18 to 64 of 143,153, participated. 510 patients with unexplained chronic fatigue were identified. 265 of these conformed to one or more case definitions. 216 were invited to join the register; 160 agreed. 96.9 % of participants conformed to the CDC 1994 (Fukuda) definition; the Canadian definition defined more precisely a subset of these. The addition of an epidemiological case definition increased case ascertainment by approximately 4%. A small-scale study in a
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".