Back Pain and Co-occurring Conditions
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional population-level health survey. OBJECTIVE: To describe the frequency of co-occurring conditions with back pain; to identify risk factors for back pain controlling for co-occurring conditions; and to examine the association between back pain and individual co-occurring conditions. SUMMARY OF BACKGROUND DATA: Back pain shares risk factors with a range of other conditions. Most studies have considered risk factors for back pain without taking into account the potential influence of co-occurring conditions. METHODS: Analysis of the 2013 Canadian Community Health Survey (n = 61,854, age ≥15 yr). Back pain status and co-occurring conditions were determined from questions about long-term health conditions diagnosed by a health profession. Multivariable log-Poisson regression analysis was used to assess the adjusted association of back pain with demographic and lifestyle characteristics and co-occurring conditions. RESULTS: The population prevalence of reported back pain was 19.3%. Most (71%) reported at least one co-occurring condition. Most frequently reported were arthritis (35%), high blood pressure (26%), migraine (18%), and mood disorders (14%). Following the addition of co-occurring condition count to the regression model, being female and being overweight/obese were no longer significantly associated with back pain, and the associations with ages 45 to 54 years and older, low-income, smoking, and being physical inactive were significantly attenuated. The highest prevalence ratio, 3.32 (95% confidence interval: 3.06-3.59), was for 3+ co-occurring conditions. In multivariable regression all but a few individual chronic conditions remained significant associated with back pain. CONCLUSION: Established risk factors for back pain may be largely a reflection of shared risk factors with co-occurring conditions. The high frequency of co-occurring conditions likely reflects diverse mechanisms related to heterogeneity of back pain. The extent of association of co-occurring conditions with back pain has implications for clinical management and need for further research to characterize subgroups. LEVEL OF EVIDENCE: 2.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".