Predictors of Back Pain in a General Population Cohort
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Brief 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. This study used longitudinal data from the Canadian National Population Health Survey. The incidence of chronic back pain in the general household population was 44.7 per 1,000 person-years. Predictors of back pain included age, self-reported health, pattern of activity, height, and psychosocial factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it