The Association Between a Lifetime History of a Work-Related Neck Injury and Future Neck Pain: A Population Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the association between a lifetime history of a work-related neck injury and the development of troublesome neck pain in the general population. METHODS: We formed a cohort of randomly sampled Saskatchewan adults with no or mild neck pain in September 1995. At baseline, participants were asked if they had ever injured their neck at work. Six and 12 months later, participants were asked if they had troublesome neck pain defined as grades II to IV on the Chronic Pain Grade Questionnaire. Multivariable Cox regression was used to estimate the association between a lifetime history of work-related neck injury and the onset of troublesome neck pain while controlling for age and sex. RESULTS: Our cohort included 866 individuals at risk for developing troublesome neck pain. Of those, 73.8% (639/866) were followed up at 6 months, and 63.0% (546/866), at 1 year. We found a positive association between a history of a work-related neck injury and the onset of troublesome neck pain (age- and sex-adjusted hazard rate ratio [HRR], 2.4; 95% confidence interval, 1.3-4.7). CONCLUSION: Our analysis suggests that a lifetime history of work-related neck injury is associated with an increased risk of troublesome neck pain. Occupational neck injuries can lead to recurrent episodes of 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".