Relationship between depressive symptoms and acute low back pain at first medical consultation, three and six weeks of primary care
Bibliographic record
Abstract
BACKGROUND: Aim of the study was to test lagged reciprocal effects of depressive symptoms and acute low back pain (LBP) across the first weeks of primary care. METHODS: In a prospective inception cohort study, 221 primary care patients with acute or subacute LBP were assessed at the time of initial consultation and then followed up at three and six weeks. Key measures were depressive symptoms (modified Zung Self-Rating Depression Scale) and LBP (sensory pain, present pain index and visual analogue scale of the Short-Form McGill Pain Questionnaire). RESULTS: When only cross-lagged effects of six weeks were tested, a reciprocal positive relationship between LBP and depressive symptoms was shown in a cross-lagged structural equation model (β = .15 and .17, p < .01). When lagged reciprocal paths at three- and six-week follow-up were tested, depressive symptoms at the time of consultation predicted higher LBP severity after three weeks (β = .23, p < .01). LBP after three weeks had in turn a positive cross-lagged effect on depression after six weeks (β = .27, p < .001). CONCLUSIONS: Reciprocal effects of depressive symptoms and LBP seem to depend on time under medical treatment. Health practitioners should screen for and treat depressive symptoms at the first consultation to improve the LBP treatment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".