Pain invalidation is an independent determinant of fibromyalgia, irrespective of depression
Bibliographic record
Abstract
Background: This study evaluated and compared invalidation domains (discounting and lack of understanding) in patients with fibromyalgia (FM) and non-FM chronic musculoskeletal pain.The relationship between invalidation and depression was also investigated to clarify the role of FM.Methods: A total of 207 patients (145 FM and 62 non-FM) completed questionnaires including the Illness Invalidation Inventory (3*I), Widespread Pain Index (WPI), Revised Fibromyalgia Impact Questionnaire (FIQR), and Beck Depression Inventory-second edition (BDI-II).Adjusted linear regression analyses were performed to assess the association between the 3*I and BDI-II, and univariate and multivariate logistic regression analyses were used to examine the relationships between FM (as the dependent variable) and other variables.Results: WPI, FIQR, BDI-II, and 3*I scores were significantly higher in FM patients than in non-FM patients.The BDI-II total score was found to be a significant predictor of discounting and lack of understanding stemming from spouse and family sources in both groups, with slightly stronger effects in the non-FM group than in FM patients.In multivariate regression analysis, discounting from family sources (odds ratio [OR] = 1.81, 95% confidence interval [CI] = 1.02-3.20,P = 0.040) and the BDI-II total score (OR = 1.12, 95% CI = 1.06-1.20,P = 0.001) remained a determinant of having FM.Conclusions: The higher frequency of invalidation in FM patients is not fully explained by depression because of weaker statistical relationships between invalidation and depression in FM rather than other pain disorders.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".