74 Does work relatedness of an injury influence time on disability benefits in Brazil, a cohort study
Bibliographic record
Abstract
Objectives Evidence on predictors of time on benefits is mainly from developed countries. Evidence from emerging economies is lacking. In this cohort study predictors for time on disability benefits were identified within a Brazilian workers compensation insurance. Workers can claim benefits for either work related or work relevant conditions. This provided us with the unique opportunity to examine the role of work relatedness of back pain on time on benefits Methods In 2008, 83,114 workers diagnosed with back pain were claiming benefits. Claimants had > 15 days away from work. The analysis was adjusted for sex, age (/ 10 year), back pain benefits in 2007, claim rate of the industrial sector in 2007, and ICD-10 diagnosis (as agreed by two physicians). Duration of follow up was 52 weeks. Predictors for time on benefits in the first episode were identified by means of Cox regression analysis. Explained variance and c-statistic were calculated. Results Median time on benefits was 55 days (Inter Quartile Range (IQR) = 33–86). 1.49% of workers was on benefits after 52 weeks. Work relatedness of the claim was associated with the outcome: those with a non work related claim returned to work 1.04 times faster compared to those with a work related condition. Only age and diagnosis had hazard rate ratios over 1.2. Explained variance of the model was 3% (c-statistic<0.6). Conclusions Work relatedness is weakly associated with the outcome. The factors in this database have little explanatory power. More information on factors like: functional status, pain, recovery expectations, availability of workplace accommodations, physical demands and health care use might result in prediction that has utility in risk stratification an referral to early and appropriate intervention. In large administrative databases, statistical significance is easily attained therefore relevance criteria should be given and model fit should always be reported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".