An Evidence-Based Care Model for Workers With Concussion
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of an evidence-based assessment program for people with workers' compensation claims for concussion on healthcare utilization and duration of lost time from work. SETTING: An assessment program for people with a work-related concussion was introduced to provide physician assessment focused on education and appropriate triage. PARTICIPANTS: A total of 3865 people with accepted workers' compensation claims for concussion with dates of injury between January 1, 2014, and February 28, 2017. DESIGN: A quasiexperimental pre-/poststudy of healthcare utilization (measured by healthcare costs) and duration of time off work (measured by loss of earnings benefits) in a cohort of people with workers' compensation claims for concussion in the period prior to and following introduction of a new assessment program. Administrative data were retrospectively analyzed to compare outcomes in patients from the preassessment program implementation period to those in the postimplementation period. RESULTS: The assessment program resulted in reduced healthcare utilization reflected by a 14.4% (95% confidence interval, -28.7% to -0.8%) decrease in healthcare costs. The greatest decrease in healthcare costs was for assessment services (-27.9%) followed by diagnostic services (-25.7%). There was no significant difference in time off work as measured by loss-of-earnings benefits. CONCLUSION: A care model for people with a work-related concussion involving an evidence-based assessment by a single physician focused on patient education resulted in significantly decreased healthcare utilization without increasing duration of time off work.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".