Outcome and Predictor Relationships in Fibromyalgia and Rheumatoid Arthritis: Evidence Concerning the Continuum versus Discrete Disorder Hypothesis
Bibliographic record
Abstract
OBJECTIVE: To compare outcome-predictor relationships in fibromyalgia (FM) and rheumatoid arthritis (RA), to provide information regarding the competing hypotheses that FM is a continuum or a discrete disorder. METHODS: We studied 3 outcome variables (work disability, opioid use, depression) and 12 clinical predictor variables in 2,046 patients with FM and 20,374 with RA. We determined whether outcome-predictor relationships were stronger in FM or RA by measuring the areas under the receiver-operating curves. We used fractional polynomial logistic regression to create graphic models for the outcome-predictor relationships. RESULTS: All measures of status and outcome were more abnormal in FM than in RA. Depression was reported in 33.4% of patients with FM compared with 15.1% of those with RA. The predictor-outcome relationship was significantly stronger in RA in 28 of the 36 tests, and not different in the remainder. The relationship between outcome and predictor variables was generally similar in patients with FM and RA. However, unmodeled depression that was not explained by study variables was noted in FM. CONCLUSION: Our data are consistent with the hypothesis that FM is the end of a severity continuum, but that additional psychological factors are an integral part of the syndrome.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".