The Relation of Physical Comorbidity and Multimorbidity to Fibromyalgia, Widespread Pain, and Fibromyalgia-related Variables
Bibliographic record
Abstract
OBJECTIVE: To investigate the relation of physical (non-psychological) comorbidity and multimorbidity to quantitative measures of fibromyalgia (FM) and musculoskeletal pain. METHODS: We studied 12,215 patients in a research databank with quantitative measures of FM-related variables (FMV) that included binary determinations of FM and widespread pain (WSP), and constituent variables of FM diagnosis that included the WSP index (WPI), the symptom severity score (SSS), and the polysymptomatic distress scale (PSD). We assessed self-reported comorbid conditions and covariates that included age, sex, body mass index, hypertension, smoking history, and total household income. We used nearest-neighbor matching and regression adjustment treatment effects models to measure the effect of comorbidities on FMV. RESULTS: We found a positive association between FMV and the probability of having each comorbid condition. Patients with ≥ 1 comorbidities had PSD, WPI, and SSS increases of 3.0 (95% CI 2.7-3.3), 1.8 (95% CI 1.6-2.0), and 1.2 (95% CI 1.1-1.3) units, respectively, and an increase in FM prevalence from 20.4% to 32.6%. As the number of comorbid conditions present increased from 1 to 4 or more, PSD, WPI, SSS, and FM percent increased stepwise. For patients with ≥ 4 conditions, the predicted prevalence of FM was 55.2%. CONCLUSION: FM and FMV are associated with an increase in the number of comorbidities, and the association can be measured quantitatively. However, the association of WSP and FM may be an effect of definitions of WSP and FM, because comorbidity increases are also present with subsyndromal levels of both conditions.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".