Association of persistent pain with the incidence of chronic conditions following a disabling work-related injury
Bibliographic record
Abstract
OBJECTIVES: In a cohort of workers disabled by a work-related injury or illness, this study aimed to: (i) compare pre-injury prevalence estimates for common chronic conditions to chronic condition prevalence in a representative sample of working adults; (ii) calculate the incidence of chronic conditions post-injury; and (iii) estimate the association between persistent pain symptoms and the incidence of common chronic conditions. METHODS: Eighteen months post-injury, 1832 workers disabled by a work-related injury or illness in Ontario, Canada, completed an interviewer-administered survey. Participants reported pre- and post-injury prevalence of seven physician-diagnosed chronic conditions, and demographic, employment, and health characteristics. Pre-injury prevalence estimates were compared to estimates from a representative sample of workers. Multivariable logistic regression was used to examine the association of persistent pain with post-injury chronic condition incidence. RESULTS: Age-standardized pre-injury prevalence rates for diabetes, hypertension, arthritis, and back problems were similar to prevalence rates observed among working adults in Ontario, while prevalence rates for mood disorder, asthma and migraine were moderately elevated. Post-injury prevalence rates of mood disorder, migraine, hypertension, arthritis, and back problems were elevated substantially in this cohort. High persistent pain symptoms were strongly associated with the 18-month incidence of these conditions. CONCLUSIONS: The incidence of five chronic conditions over an 18-month follow-up period post injury was substantial. Persistent pain at 18 months was associated with this elevated incidence, with population attributable fraction estimates suggesting that 37-39% of incident conditions may be attributed to exposure to high levels of persistent pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".