Quality of life and cost consequence of delays in endovascular treatment for acute ischemic stroke in China
Bibliographic record
Abstract
BACKGROUND: Although endovascular therapy (EVT) improves clinical outcomes in patients with acute ischemic stroke, the time of EVT initiation significantly influences clinical outcomes and healthcare costs. This study evaluated the impact of EVT treatment delay on cost-effectiveness in China. METHODS: A model combining a short-term decision tree and long-term Markov health state transition matrix was constructed. For each time window of symptom onset to EVT, the probability of receiving EVT or non-EVT treatment was varied, thereby varying clinical outcomes and healthcare costs. Clinical outcomes and cost data were derived from clinical trials and literature. Incremental cost-effectiveness ratio and incremental net monetary benefits were simulated. Deterministic and probabilistic sensitivity analyses were performed to assess the robustness of the model. The willingness-to-pay threshold per quality-adjusted life-year (QALY) was set to ¥71,000 ($10,281). RESULTS: EVT performed between 61 and 120 min after the stroke onset was most cost-effective comparing to other time windows to perform EVT among AIS patients in China, with an ICER of ¥16,409/QALY ($2376) for performing EVT at 61-120 min versus the time window of 301-360 min. Each hour delay in EVT resulted in an average loss of 0.45 QALYs and 165.02 healthy days, with an average net monetary loss of ¥15,105 ($2187). CONCLUSIONS: Earlier treatment of acute ischemic stroke patients with EVT in China increases lifetime QALYs and the economic value of care without any net increase in lifetime costs. Thus, healthcare policies should aim to improve efficiency of pre-hospital and in-hospital workflow processes to reduce the onset-to-puncture duration in China.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".