Cost-effectiveness of a high-intensity rapid access outpatient stroke rehabilitation program
Bibliographic record
Abstract
A common strategy to improve cost-effectiveness in healthcare is to offer outpatient care instead of in-hospital care. Toronto Rehabilitation Institute developed an outpatient high-intensity fast-track (FT) stroke rehabilitation program aimed at discharging inpatient stroke rehabilitation patients earlier or bypassing inpatient rehabilitation altogether. This cost-effectiveness analysis compares FT rehabilitation within 1 week of discharge with no FT in a single healthcare payer system. Patient costs and outcomes over a 12-week time horizon were included. Using individual-level FT data from April 2015 to March 2016, incremental cost-effectiveness ratios (ICERs) (with 95% confidence interval) were estimated using regression. Subgroup analysis was completed for patients entering FT directly from inpatient rehabilitation and acute stroke care. Uncertainty was assessed using a cost-effectiveness acceptability curve with a range of willingness-to-pay values ($0-1000 per inpatient day saved). ICER (95% confidence interval) estimate for patients entering FT from inpatient rehabilitation was $404 ($270-620) per inpatient day saved. ICER estimate for direct from acute care admissions was $37 ($20-55) per day saved. At willingness-to-pay of $698 (cost of one alternate level of care day in acute care awaiting rehabilitation), the probability of FT being cost-effective was 99.2 and 100% for patients from inpatient rehabilitation and acute stroke care, respectively. From a single healthcare payer perspective, FT is a cost-effective method of providing appropriate rehabilitation intensity for stroke patients early on, and likely to provide savings to the healthcare system upstream through fewer days awaiting rehabilitation admission.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".