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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".