Predicting return to work following treatment of chronic pain disorder
Bibliographic record
Abstract
BACKGROUND: The care of injured workers with chronic pain remains an important public health issue given its increasing prevalence. The consequences often include loss of self-esteem and stress in family relationships. AIMS: To report our interdisciplinary approach to the care of chronic pain disorder (CPD) and describe the predictors associated with a successful return to work (RTW). METHODS: Relevant covariates, including demographic data, time from injury, and functional scores were recorded for clients injured at work in Ontario, Canada. Our primary outcome, RTW, was assessed at 3 months post-discharge. Descriptive statistics and logistic regression were used to identify those factors predicting a successful RTW. RESULTS: Of the injured workers who participated in the interdisciplinary CPD treatment programme, 1002 clients met our inclusion criteria and were included in the study. Fifty-five per cent were male with a mean age of 46 years. Median time from injury to treatment was 720 days. At 3 months post-treatment, 136 (14%) of the participants were working. Multivariable logistic regression revealed that earlier time since injury (OR = 0.71, 95% CI 0.55-0.92) and presence of an RTW coordinator (RTWC) (OR = 3.42, 95% CI 2.08-5.63) were significant predictors of successful RTW. There was also a significant interaction between RTWC involvement and time since injury. The latter did not appear to influence the likelihood of RTW when an RTWC was present. CONCLUSIONS: Workers compensation boards should refer injured workers with CPD to treatment programmes as early as possible to achieve a successful RTW. Additionally, RTWCs play an important role in improving work outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".