Bibliographic record
Abstract
Fibromyalgia (FM) is relatively common, with prevalence estimates between 2.0% and 3.5%1,2,3. Its prevalence increases with age, up to around age 60 to 70 years; FM is roughly 5 times more common in women than in men4, and is one of the most common reasons for referral to a rheumatologist5. The etiology and pathophysiology of FM are not well determined, and while there are several extant theories — including muscle dysfunction or misuse, central sensitization, sleep disorders, and altered stress axis function — there is little consistent evidence to support, or refute, any of these. Although FM is often insidious in onset, it is part of the human condition to look for a cause for one’s symptoms, and many patients report “trigger” events on which to blame their symptoms. Bennett, et al reported results of a large Internet survey of 2569 participants, and presented data on the 13 most commonly reported FM trigger events among questionnaire respondents (most of whom probably had FM, and most of whom probably completed the questionnaire only once, although one cannot be certain)6. Chronic stress (reported by 42%) and emotional trauma (31%) were the most frequently reported triggers. (The trigger events were not mutually exclusive.) However, physical trauma was also commonly reported, either due to or not due to, a motor vehicle accident (MVA; 17% and 16%, respectively). In this issue … Address correspondence to G. Jones; E-mail: gareth.jones{at}abdn.ac.uk
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".