The Six-Month Incidence of Clinically Significant Low Back Pain in the Saskatchewan Adult Population
Bibliographic record
Abstract
STUDY DESIGN: A population-based, longitudinal, mailed survey was conducted. OBJECTIVE: To investigate the 6-month incidence and determinants of clinically significant low back pain in the Saskatchewan adult population. SUMMARY OF BACKGROUND DATA: Few studies have investigated the incidence of significant low back pain in general populations. When available, such studies often differ in the assessment of pain severity. This lack of consensus in measuring pain severity results in large differences in incidence rates. METHODS: A questionnaire requesting information on low back pain and other health conditions was mailed to randomly chosen individuals, ages 20 to 69, residing in the province of Saskatchewan, Canada. Of the 1131 (55%) who responded at baseline, 848 had not experienced clinically significant low back pain during the past 6 months. Clinically significant low back pain was assessed using the Chronic Pain Questionnaire, a 7-item scale that measures the intensity of chronic pain and associated disability. Individuals with no clinically significant low back pain were followed up at 6 months. RESULTS: At the follow-up assessment, 50 individuals reported clinically significant low back pain, representing a cumulative incidence of 8% (95% confidence interval, 6-10.4). In logistic regression models, marital status, rural residency, and history of back and neck pain were associated with the onset of clinically significant low back pain. CONCLUSIONS: The 6-month incidence of clinically significant low back pain is high in Saskatchewan. It is important to prevent this condition because of the high economic and social costs associated with it.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".