The Use of Polysymptomatic Distress Categories in the Evaluation of Fibromyalgia (FM) and FM Severity
Bibliographic record
Abstract
OBJECTIVE: The polysymptomatic distress (PSD) scale is derived from variables used in the 2010 American College of Rheumatology (ACR) fibromyalgia (FM) criteria modified for survey and clinical research. The scale is useful in measuring the effect of PSD over the full range of pain-related clinical symptoms, not just in those who are FM criteria-positive. However, no PSD scale categories have been defined to distinguish severity of illness in FM or in those who do not satisfy the FM criteria. We analyzed the scale and multiple covariates to develop clinical categories and to further validate the scale. METHODS: FM was diagnosed according to the research criteria modification of the 2010 ACR FM criteria. We investigated categories in a large database of patients with pain (2732 with rheumatoid arthritis) and developed categories by using germane clinic variables that had been previously studied for severity groupings. By definition, FM cannot be diagnosed unless PSD is at least 12. RESULTS: Based on population categories, regression analysis, and inspections of curvilinear relationships, we established PSD severity categories of none (0-3), mild (4-7), moderate (8-11), severe (12-19), and very severe (20-31). Categories were statistically distinct, and a generally linear relationship between PSD categories and covariate severity was noted. CONCLUSION: PSD categories are clinically relevant and demonstrate FM type symptoms over the full range of clinical illness. Although FM criteria can be clinically useful, there is no clear-cut symptom distinction between FM (+) and FM (-), and PSD categories can aid in more effectively classifying patients.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".