Cost-benefit analysis of stroke rehabilitation in Iran
Bibliographic record
Abstract
BACKGROUND: The economic evaluation of medication interventions for stroke has been the subject of much economic research. This study aimed to examine the cost-benefit of multidisciplinary rehabilitation services for stroke survivors in Iran. METHODS: This economic evaluation was conducted from the payer's perspective with a lifetime horizon in Iran. A Markov model was designed and Quality-adjusted life years (QALYs) were the final outcomes. First, to evaluate the cost-effectiveness, the incremental cost-effectiveness ratio (ICER) was calculated. Then, using the average net monetary benefit (NMB) of rehabilitation, the average Incremental Net Monetary Benefit (INMB) per patient was calculated. The analyses were carried out separately for public and private sector tariffs. RESULTS: While considering public tariffs, the rehabilitation strategy had lower costs (US$5320 vs. US$ 6047) and higher QALYs (2.78 vs. 2.61) compared to non-rehabilitation. Regarding the private tariffs, the rehabilitation strategy had slightly higher costs (US$6,698 vs. US$6,182) but higher QALYs (2.78 vs. 2.61) compared to no rehabilitation. The average INMB of rehabilitation vs non-rehabilitation for each patient was estimated at US$1518 and US$275 based on Public and private tariffs, respectively. CONCLUSION: Providing multidisciplinary rehabilitation services to stroke patients was cost-effective and has positive INMBs in public and private tariffs.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 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".