Differences and similarities among questionnaires to assess pain status in chronic widespread pain population: a quantitative analysis
Bibliographic record
Abstract
OBJECTIVE: In clinical practice, multiple questionnaires are often used as part of the diagnosis of chronic widespread pain. Body Surface Area (BSA), Visual Analogue Scale (VAS), Fibromyalgia Diagnostic Criteria (FDC) and Central Sensitization Inventory (CSI) have all been used as screening tools to assess pain status in individuals with widespread pain. However, substantial overlap can be observed among these commonly employed questionnaires. This study aimed to quantitatively determine the most independent and dependent clinical characteristics obtained through these questionnaires and to examine potential redundancies. METHODS: Seventy-nine participants with widespread pain, 61 females and 18 males, from a chronic pain outpatient clinic were recruited. The FDC, BSA, VAS and the CSI were measured for all participants. A principal component analysis (PCA) using a varimax rotation was used to determine which clinical measures represented separate constructs of widespread pain. This was followed by a regression analysis to assess redundancy between the constructs and related pain characteristics. RESULTS: The identified three-component PCA solution was characterized by (1) the FDC and CSI score, (2) the VAS score and (3) the BSA score. This indicates that the BSA and the VAS scores capture independent patient information. From the regression analysis, the FDC and CSI scores shared approximately 80% of the variance, indicative of substantial overlap between scores. CONCLUSION: Our findings demonstrated that BSA and VAS scores were independent clinical measures of widespread chronic pain, while the FDC and CSI scores were not independent, were highly correlated and provided redundant information. Clinicians should continue using both the BSA and VAS; however, either only FDC or CSI will be beneficial during clinical assessment of widespread chronic pain.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".