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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".