The Association Between Self-Reported Cardiovascular Disorders and Troublesome Neck Pain: A Population-Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this population-based cohort study was to investigate the association between self-reported cardiovascular disorders and troublesome neck pain. METHODS: Using data from the Saskatchewan Health and Back Pain Survey (1995), we formed a cohort of 922 randomly sampled Saskatchewan adults with no or mild neck pain. We used the Comorbidity Questionnaire to measure the point prevalence of self-reported cardiovascular disorders and classified them into 3 levels of severity: (1) absent, (2) present but does not or mildly impacts on my health, and (3) present and moderately or severely impacts on my health. Six and 12 months later, we measured the presence of troublesome neck pain (grades II-IV) using the Chronic Pain Questionnaire. Multivariable Cox regression was used to estimate the association between cardiovascular disorders and the troublesome neck pain while controlling for confounders. RESULTS: The follow-up rate was 73.8% (680/922) at 6 months and 62.7% (578/922) at 1 year. No association was found between self-reported cardiovascular disorders that had no or mild impact on health and the onset of troublesome neck pain. We found a crude association between self-reported cardiovascular disorders that moderately or severely impacted health and the onset of troublesome neck pain (crude hazard rate ratio, 4.3; 95% confidence interval, 1.8-10.0). The association was positively confounded by age, sex, and education (adjusted hazard rate ratio, 5.9; 95% confidence interval, 2.3-14.9). CONCLUSIONS: Our analysis suggests that self-reported cardiovascular disorders that moderately or severely impact one's health are a risk factor for developing troublesome neck pain.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".