Comparison of Physician‐Based and Patient‐Based Criteria for the Diagnosis of Fibromyalgia
Bibliographic record
Abstract
OBJECTIVE: The American College of Rheumatology (ACR) 2010 preliminary fibromyalgia diagnostic criteria require symptom ascertainment by physicians. The 2011 survey or research modified ACR criteria use only patient self-report. We compared physician-based (MD) (2010) and patient-based (PT) (2011) criteria and criteria components to determine the degree of agreement between criteria methodology. METHODS: We studied prospectively collected, previously unreported rheumatology practice data from 514 patients and 30 physicians in the ACR 2010 study. We evaluated the widespread pain index, polysymptomatic distress (PSD) scale, tender point count (TPC), and fibromyalgia diagnosis using 2010 and 2011 rules. Bland-Altman 95% limits of agreement (LOA), kappa statistic, Lin's concordance coefficient, and the area under the receiver operating curve (ROC) were used to measure agreement and discrimination. RESULTS: MD and PT diagnostic agreement was substantial (83.4%, κ = 0.67). PSD scores differed slightly (12.3 MD, 12.8 PT; P = 0.213). LOA for PSD were -8.5 and 7.7, with bias of -0.42. The TPC was strongly associated with both the MD (r = 0.779) and PT PSD scales (r = 0.702). CONCLUSION: There was good agreement in MD and PT fibromyalgia diagnosis and other measures among rheumatology patients. Low bias scores indicate consistent results for physician and patient measures, but large values for LOA indicate many widely discordant pairs. There is acceptable agreement in diagnosis and PSD for research, but insufficient agreement for clinical decisions and diagnosis. We suggest adjudication of symptom data by patients and physicians, as recommended by the 2010 ACR criteria.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".