A prediction rule for duration of disability benefits in workers with nonspecific low back pain
Bibliographic record
Abstract
Objectives This study examines factors collected within the first 4 weeks of work disability to predict outcomes for workers on benefits due to back pain at 6 & 24 months. Methods Routinely collected information from the compensation agency on: age, gender, occupation, earnings, nature and event of injury, language, job tenure, history of prior claims, physical work, healthcare and opioid use will be analysed. Additional data was extracted via claim file review: need for a translator, previous claims in other jurisdictions, workplace offer of modified work, and work limitations suggested by health professional. Data from the Readiness for Return to Work cohort study on health, functional status pain, mental health, expectations for recovery, job satisfaction, and supervisor9s response are available for a subset of workers. Benefits status at 6 months will be studied using logistic regression. Cox regression and mover stayer models will be used for time on disability benefits and recurrences during the 2 years of follow-up. Results 6657 workers were selected and additional information was entered in the research database. 1796 were still on full benefits at 4 weeks. 38% were female, 81% of workplace reported to offer workplace accommodation, 48% of workers were represented by a union, 5056 prescriptions for oxycodone were reimbursed in the first 4 weeks. 300 workers were still off work 6 months after date of injury. Conclusions A prediction tool that provides projections of different injured worker outcomes such as time remaining on benefits and likelihood of a recurrence will be presented.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".