Depression as a risk factor for onset of an episode of troublesome neck and low back pain
Bibliographic record
Abstract
The objective of this study is to determine whether depression is an independent risk factor for onset of an episode of troublesome neck and low back pain. There is growing evidence that pain problems increase the risk of depression. However, the evidence about the role of depression as a risk factor for onset of pain problems is contradictory. This lack of consistency in research findings may be due in part to methodological weaknesses in existing studies, for example, use of an inappropriate study design and inadequate consideration of confounding. A population-based random sample of adults was surveyed and followed at 6 and 12 months. Individuals at risk of troublesome (intense and/or disabling) neck or low back pain are the subjects of this report (n=790). We used Cox proportional hazards models to measure the time-varying effect of depressive symptoms on the onset of troublesome neck and low back pain. Our multivariable analysis considered the possible confounding effects of demographic and socio-economic factors, health status, co-morbid medical conditions and injuries to the neck or low back. We found an independent and robust relationship between depressive symptoms and onset of an episode of pain. In comparison with the lowest quartile of scores (the least depressed), those in the highest quartile of depression scores had a four-fold increased risk of troublesome neck and low back pain (adjusted HRR 3.97; 95% CI 1.81-8.72). Depression is a strong and independent predictor for the onset of an episode of intense and/or disabling neck and low back 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".