Predictors of Back Pain in a General Population Cohort
Bibliographic record
Abstract
STUDY DESIGN: The study used longitudinal data from the first and second cycles (1994-1995 and 1996-1997) of the Canadian National Population Health Survey. OBJECTIVE: Our objective was to derive prediction models for back pain in the general male and female household populations. SUMMARY OF BACKGROUND DATA: Little is known about the predictors of back pain in the general population. Most previous studies focused on specific occupational groups and used a cross-sectional or case-control design. METHODS: The study cohort consisted of all respondents aged 18+ years who reported no back problems in the 1994-1995 National Population Health Survey cycle (N = 11,063). Potential predictors of chronic back pain were classified into nine groups and entered into stepwise logistic regression models. Bootstrap methods were used to derive the final models and assess their predictive power. RESULTS: The overall incidence of back pain was 44.7 per 1,000 person-years and was higher in women (47.0 per 1,000 person-years) compared with men (42.2 per 1,000 person-years). In men, significant predictors of back pain were age (peak effect in 45-64 years), height, self-rated health, usual pattern of activity (especially heavy work), yard work or gardening (negative association), and general chronic stress. In women, significant factors were self-reported restrictions in activity, being diagnosed with arthritis, personal stress, and history of psychological trauma in childhood or adolescence. CONCLUSIONS: Overall health and psychosocial factors are important predictors of back pain in both men and women. Other risk factors differ between the two sexes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".