Prevalence of claims-based recurrent low back pain in a Canadian population: A secondary analysis of an administrative database
Bibliographic record
Abstract
BACKGROUND: There is a vast literature reporting that the point prevalence of low back pain (LBP) is high and increasing. It is also known that a large proportion of acute LBP episodes are recurrent within 12 months. However, few studies report the annual trends in the prevalence of recurrent LBP or describe these trends according to age and sex categories. METHODS: We conducted a retrospective cohort study involving 401 264 adults selected from the administrative database of physician claims for the province of Quebec, Canada. These adults, aged 18 years and over, met the criteria of having consulted a physician three times within a 365-day period between 2000 and 2007 for a LBP condition corresponding to ICD-9 codes 721, 722, 724 or 739. All data were analyzed by sex and clustered according to specific age categories. RESULTS: We observed a decrease from 1.64% to 1.33% in the annual prevalence between 2000 and 2007 for men. This decrease in prevalence was mostly observed between 35 and 59 years of age. Older (≥ 65 years) women were 1.35 times more at risk to consult a physician for LBP in a recurrent manner than older men. The most frequently reported diagnosis was non-specific LBP between 2000 to 2007. During the same period, sequelae of previous back surgery and spinal stenosis were the categories with the largest increases. CONCLUSION: The annual prevalence of claims-based recurrent LBP progressively decreased between 2000 and 2007 for younger adults (<65 years) while older adults (≥ 65 years) showed an increase. Given the aging Canadian population, recurrent low back pain could have an increasing impact on the quality of life of the elderly as well as on the healthcare system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".