Bibliographic record
Abstract
When the Arthritis Foundation tried to categorize the 150 different forms of musculoskeletal conditions in 1963, it created a classification known as soft tissue rheumatism. Included in this listing are conditions in which joints are not involved. Soft tissue rheumatism encompasses the supporting structures of joints (e.g., ligaments, bursae, and tendons), muscles, and other soft tissues. Fibromyalgia is a form of soft tissue rheumatism. A combination of three terms—fibro (from the Latin fibra, or fibrous tissue), myo- (the Greek prefix myos, for muscles), and algia (from the Greek algos, which denotes pain)—fibromyalgia replaces earlier names for the syndrome that are still used by doctors and other health professionals such as myofibrositis, myofascitis, muscular rheumatism, fibrositis, and generalized musculoligamentous strain. Fibromyalgia is not a form of arthritis, since it is not associated with joint inflammation. In the late 1980s, a Multicenter Criteria Committee under the direction of Dr. Frederick Wolfe at the University of Kansas was formed to define fibromyalgia. In their study, 293 patients with presumed fibromyalgia were compared with 265 patients who had other rheumatic diseases in 16 centers throughout North America. The groups were evaluated for a variety of symptoms, signs, and laboratory abnormalities in an effort to ascertain which factors were the most sensitive and specific for defining the disorder. In other words, the investigators wanted to identify the most frequently found features of fibromyalgia (sensitivity) that could help doctors differentiate it from other disorders (specificity). The list in Table 1 was 88.4 percent sensitive and 81.1 percent specific in identifying fibromyalgia patients. As a result, these criteria were endorsed in 1990 by the American College of Rheumatology (ACR), the association to which nearly all 5,000 rheumatologists in the United States and Canada belong. Focusing on Table 1 and Figure 3, fibromyalgia essentially is: 1. Widespread pain of at least 3 months’ duration (this rules out viruses or traumatic insults which resolve on their own). 2. Pain in all four quadrants of the body (picture cutting the body into quarters, as in a pie): right side, left side, above the waist, below the waist.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".