Early Prognostic Factors for Claim Cost and Claim Duration Following a Work-Related Back Injury in Saskatchewan, Canada
Bibliographic record
Abstract
PURPOSE: The objective of the current study was to determine the degree to which individual prognostic factors obtained within the first 3-4 weeks of the initiation of a work-related back injury claim can predict claim cost and claim duration. METHODS: Prognostic factor data and outcome data regarding claim cost and duration were obtained from back injury claimants via an online questionnaire and the local workers' compensation board. Regression models were used to determine which of the factors were best able to predict claim cost, claim duration, and chronic work disability. RESULTS: Age, disability, and an accommodation and/or early return-to-work program being offered were included in the three final regression models and were therefore deemed to be best able to predict all three outcomes. Recovery expectations was also included in the final regression model for claim duration and is therefore able to assist in the prediction of this outcome. CONCLUSION: The regression models produced in the current study could be used to formulate equations to estimate claim cost and duration, thereby allowing insurers to identify "high-risk claims" early in the claim process and facilitate more targeted interventions in such cases. As well, whether an accommodation and/or early return-to-work program is offered is highlighted as a modifiable risk factor that could be used by insurers, employers, and workers to reduce claim cost and claim duration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".