You get used to it, or do you: symptom length predicts less fibromyalgia physical impairment, but only for those with above-average self-efficacy
Bibliographic record
Abstract
To determine whether the effects of symptom duration on fibromyalgia physical impairment are moderated by symptom self-efficacy, data from 572 female participants, who were members of a large health maintenance organization and had a diagnosis of fibromyalgia syndrome (FMS) were assessed. Age, symptom duration, history of physical, sexual, and emotional abuse, fibromyalgia-specific self-efficacy (Arthritis Self-Efficacy Scale adapted for FMS [ASES]), depression (Centers for Epidemiological Studies Depression Scale [CES-D]), fibromyalgia physical impairment (Fibromyalgia Impact Questionnaire [FIQ]), and pain (McGill Present Pain Index [PPI]) were measured five times across 18 months. Linear regressions were performed to predict baseline FIQ and PPI cross-sectionally. Of primary interest was a hypothesized interaction between ASES and symptom duration, which was significant in relation to FIQ but not PPI. Multilevel mixed models were performed to determine whether the same pattern existed longitudinally controlling for baseline symptom duration as an effect of time and ASES. The interaction was significant in the models for both FIQ and PPI. These results suggest that the effects of age and symptom duration on FMS are unique, and that self-efficacy plays a crucial role in moderating disease course (measured by symptom duration or time) in FMS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".