Abstract WMP43: Optimizing Therapy Intensity For Inpatient Stroke Rehabilitation: A Linear Programming Model For Cost
Bibliographic record
Abstract
Background: Although the best practice guidelines in Canada recommended that stroke patients in inpatient rehabilitation receive a minimum of 3 hours of therapy 5 days per week, very few patients receive this amount of therapy as the national average is 90 minutes of therapy 5 days a week. Administrators have stated that is not feasible to provide the best practice amounts of therapy due to budget constraints. Therefore, the purpose of this study was to develop a linear programming model which assessed the feasibility of meeting Canadian guidelines for therapy amount in inpatient stroke rehabilitation. Methods: A linear programming model was developed based upon previously published works on the impacts of increasing therapy amount to the Canadian best practices. The objective function was to minimize cost. Decision variables included the hours of physiotherapy, occupational therapy, and speech-language pathology a moderate patient receives. Constraints included minimum and maximum hours of therapy, the relationship between therapy amount of length of stay, and the therapy time ratios Results: The optimal solution found that cost, when minimized with moderate patients, were provided with 1.2, 1.2, and 0.6 hours of physiotherapy, occupational therapy, and speech-language pathology respectively. This results in a 24.3-day length of stay. The total modeled cost was $18,253.55 per moderate patient. Based upon this model, the current Canadian average length of stay and therapy amount costs an additional $1,470.13 per moderate patient compared to the model solution. Conclusion: This work demonstrates that providing the best practice amount of therapy to patients may result in cost savings due to the reduction in length of stay, contradicting the commonly held notion that providing more therapy is not feasible from a financial point of view.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".