A Data-Driven Biopsychosocial Framework Determining the Spreading of Chronic Pain
Bibliographic record
Abstract
Abstract Chronic pain conditions are complex syndromes characterized by a mosaic of biological, psychological, and social factors. We derived predictive models for the number of co- existing pain sites in the UK Biobank and identified a common risk score that classified different chronic pain conditions in cross-sectional data, predicted the development of chronic pain in pain-free individuals, and determined the spreading of chronic pain to multiple sites or its recovery nine years later. The features with the strongest prognosis included sleeplessness, feeling ‘fed-up’, tiredness, stressful life events, and a BMI > 30. The risk score for pain was associated with an inflammatory blood marker, a polygenic risk score for pain, and a neuroimaging-based marker for sustained pain. The demonstration of a common biopsychosocial risk factor for different clinical pain conditions may help better characterize a general chronic pain syndrome, tailor research protocols, optimize patient randomization in clinical trials, and improve pain management.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".