Cost-Effectiveness of Optimizing Acute Stroke Care Services for Thrombolysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Thrombolysis in acute stroke is effective up to 4.5 hours after symptom onset but relies on early recognition, prompt arrival in hospital, and timely brain scanning. This study aimed to establish the cost-effectiveness of increasing thrombolysis rates through a series of hypothetical change strategies designed to optimize the acute care pathway for stroke. METHODS: A decision-tree model was constructed, which relates the acute management of patients with suspected stroke from symptom onset to outcome. Current practice was modeled and compared with 7 change strategies designed to facilitate wider eligibility for thrombolysis. The model basecase consisted of data from consenting patients following the acute stroke pathway recruited in participating hospitals with data on effectiveness of treatment and costs from published sources. RESULTS: All change strategies were cost saving while increasing quality-adjusted life years gained. Using realistic estimates of effectiveness, the change strategy with the largest potential benefit was that of better recording of onset time, which resulted in 3.3 additional quality-adjusted life years and a cost saving of US $46,000 per 100,000 population. All strategies increased the number of thrombolysed patients and the number requiring urgent brain imaging (by 9% to 21% dependent on the scenario). Assuming a willingness-to-pay of US $30,000 per quality-adjusted life year gained, the potential budget available to deliver the interventions in each strategy ranged from US $50,000 to US $144,000. CONCLUSIONS: These results suggest that any strategy that increases thrombolysis rates will result in cost savings and improved patient quality of life. Healthcare commissioners could consider this model when planning improvements in stroke care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".