More data on major depression as an antecedent risk factor for first onset of chronic back pain
Bibliographic record
Abstract
BACKGROUND: Few epidemiological studies have examined the temporal relationship between chronic pain and depression using longitudinal data. In the present study, we examined major depression as both an antecedent risk factor and consequence of chronic back pain (CBP) in the general population. METHOD: Data on 9909 pain-free individuals 15 years and older with no history of back problems were drawn from cycle 1 of the National Population Health Survey and followed up 24 months later. Major depression was assessed using a structured diagnostic interview. RESULTS: At cycle 2, the rate of new cases of CBP in persons who were depressed at cycle 1 was 3.6% compared to 1.1% in non-depressed persons. Compared to pain-free individuals, new cases of CBP were more likely to perceive their health status as poor or fair at cycle 1, were less likely to be working, reported more chronic health problems, and sustained a back or neck injury in the preceding 12 months. After controlling for other factors, pain-free individuals diagnosed as major depressed at cycle 1 were almost three times more likely (OR 2.9, 95% CI 1.2-7.0) to develop CBP at cycle 2. CONCLUSIONS: Consistent with other longitudinal studies major depression increases the risk of developing future chronic pain. The causal mechanism linking these conditions is unknown however depression may represent a modifiable risk factor in the development of CBP.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".