Are painDETECT scores in musculoskeletal disorders associated with duration of daily pain and time elapsed since current pain onset?
Bibliographic record
Abstract
Abstract Objectives: We aimed to compare painDETECT scores in outpatients seen in a rheumatology department over a 1-month period and search for correlations between painDETECT scores and the estimated duration of daily pain and time elapsed since the onset of current pain. Patients and Methods: A total of 529 of 738 outpatients agreed to complete a set of questionnaires, including painDETECT. Results: The mean painDETECT score was 14.14 ± 7.59, and 31% of the patients had painDETECT scores of >18. Fibromyalgia ranked first (21.2 ± 6.0), followed by osteoarthritis of the lower limbs (17.8 ± 8.2), back pain and radiculopathies (16.1 ± 6.8), osteoarthritis of the upper limbs (15.7 ± 8.1), spondylarthrosis (15.1 ± 7.2), entrapment neuropathies (14.1 ± 2.4), rheumatoid arthritis (13.8 ± 7.1), miscellaneous conditions (13.8 ± 8.2), tendinitis (13.4 ± 7.9), connectivitis (11.5 ± 6.7), and osteoporosis (8.5 ± 6.9). The duration of daily pain was much longer in patients with painDETECT scores of >18 (12.41 ± 8.45 vs 6.53 ± 7.45 hours) (t = 0.0000), but very similar painDETECT scores were observed for patients suffering from pain for less than 1 week (13.7 ± 8.2; 38% > 18), for 1 month (14.5 ± 8.2; 25% > 18), several months (12.7 ± 7.3; 23% > 18), 1 year (13.8 ± 7.7; 29% > 18), or several years (14.7 ± 7.4; 33% > 18). Conclusion: PainDETECT scores differed little depending on the musculoskeletal condition, strongly correlated with the duration of daily pain, and appeared to be as high in patients with recent pain as in those suffering for years.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".