Trends in Components of Medical Spending Within Workers Compensation: Results From 37 States Combined
Bibliographic record
Abstract
OBJECTIVE: This study provides estimates of the factors that are contributing to the escalation of medical costs. DESIGN: Measures of price and utilization trends were developed to estimate their contributions to increases in workers compensation medical severity overall and across a range of services and diagnoses. PATIENTS: Analysis utilized medical transactions data covering approximately 327,000 closed claims for injuries occurring in 37 states in 1996, 1997, 2001, and 2002 provided to the National Council on Compensation Insurance by several large workers' compensation insurance companies. MAIN RESULTS: Increases in billed medical treatments per claim contributed more than half, a shift to more costly injuries accounted for a fifth, and the increase in the average cost-per-treatment generated about a quarter of the increase in medical severity between 1996-1997 and 2001-2002. CONCLUSIONS: Increases in billed medical treatments is the major cost driver in workers compensation medical costs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".