Cost-effectiveness analysis of tissue plasminogen activator for acute ischemic stroke: a comparative review.
Bibliographic record
Abstract
PURPOSE: This work was undertaken to review current evidence of cost-effectiveness analysis (CEA) on thrombolysis for acute ischemic stroke. METHODS: An electronic search via PubMed, from 1995 until May 2004, was performed. The methods undertaken by these studies were examined with particular attention to their modeling assumptions, sources of data, and outcome measures. RESULTS: Three comprehensive CEAs of rtPA (recombinant tissue plasminogen activator) for acute ischemic stroke were reviewed. These studies were from the United States, Canada, and the United Kingdom. All these studies employed the perspective of a healthcare system and used a Markov decision-analytic modelling approach. Estimates of effectiveness of rtPA were based on the National Institute of Neurological Disorders and Stroke (NINDS) rtPA Stroke Trial, literature-derived values or a stroke registry. In each study, functional outcome measured by the modified Rankin Scale was used to represent health states, and quality-adjusted life year gained was the health outcome summary measure. The cost-effectiveness of rtPA therapy varied in magnitude, but seemingly with same positive implications. CONCLUSIONS: Cost-effectiveness analysis requires information on an intervention's effectiveness and country-specific sources of epidemiological and resource utilization data, most of which are not yet available in Taiwan. Despite the limitations, CEA is essential if a healthcare system would like to contain costs while maintaining, or improving, quality of care.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".