A Biomarker-Centric Framework for the Prediction of Future Chronic Pain
Bibliographic record
Abstract
Abstract Chronic pain is a multifactorial condition presenting significant diagnostic and prognostic challenges. Biomarkers for the classification and the prediction of chronic pain are therefore critically needed. In this multi-dataset study of over 523,000 participants, we applied machine learning to multi-dimensional biological data from the UK Biobank to identify biomarkers for 35 medical conditions associated with pain (e.g., clinical diagnosis of rheumatoid arthritis, fibromyalgia, stroke, gout, etc.) or self-reported chronic pain (e.g., back pain, knee pain, etc). Biomarkers derived from blood immunoassays, brain and bone imaging, and genetics were effective in predicting medical conditions associated with chronic pain (area under the curve (AUC) 0.62-0.87) but not self-reported pain (AUC 0.50-0.62). Among the biomarkers identified was a composite blood-based signature that predicted the onset of various medical conditions approximately nine years in advance (AUC 0.59-0.72). Notably, all biomarkers worked in synergy with psychosocial factors, accurately predicting both medical conditions (AUC 0.69– 0.91) and self-report pain (AUC 0.71–0.92). Over a period of 15 years, individuals scoring high on both biomarkers and psychosocial risk factors had twice the cumulative incidence of diagnoses for pain-associated medical conditions (Hazard Ratio (HR): 2.26) compared to individuals scoring high on biomarkers but low on psychosocial risk factors (HR: 1.06). In summary, we identified various biomarkers for chronic pain conditions and showed that their predictive efficacy heavily depended on psychological and social influences. These findings underscore the necessity of adopting a holistic approach in the development of biomarkers to enhance their clinical utility.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".