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 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".