Bibliographic record
Abstract
In this issue of The Journal , Gibson, et al have demonstrated that certain subscales of the Multidimensional Health Assessment Questionnaire (MDHAQ) can be combined to provide clues to the diagnosis of comorbid fibromyalgia (FM) in patients with rheumatic diseases1. An FM assessment screening tool (FAST) compared favorably to the 2011 self-report FM criteria, developed for clinical and epidemiologic studies2, and both agreed moderately with the clinical diagnosis of FM. The authors suggest that because MDHAQ is already frequently used in rheumatology centers, adapting the FAST indices can alert clinicians to concurrent FM without adding new self-administered screening instruments. This paper also reconsiders a number of important issues involving rheumatologists and FM. First, rheumatologists have become more aware of the frequency and effect of FM in every rheumatic disease. Whether using the FAST indices, the 2011 FM criteria, or the gold standard (Dr. Gibson’s clinical diagnosis), FM was present in about 20–30% of patients with rheumatic disease. This is consistent with reports of FM in 13–40% of cases of rheumatoid arthritis (RA)3,4, 10–20% of osteoarthritis (OA)5, 10–30% with psoriatic arthritis or a spondyloarthropathy [SpA; such … Address correspondence to Dr. D.L. Goldenberg, 1125 NW 12th Ave., Unit 107, Portland, Oregon 97209, USA. E-mail: dongoldenberg44{at}gmail.com
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".