Design of the Economic Evaluation for the Interventional Management of Stroke (III) Trial
Bibliographic record
Abstract
RATIONALE: Stroke is a common and costly condition where an effective early treatment may be expected to affect patients' future quality of life, the cost of acute medical treatment, and the cost of rehabilitation and any supportive care needed for their remaining lifetime. To assist in informing discussions on early adoption of potential treatments, economic analyses should accompany investigations that seek to improve outcomes for stroke patients. AIMS: The primary aim is to assess whether i.v./i.a. rt-PA therapy is cost-effective at 3 months compared with i.v. rt-PA, and provides cost-savings or is cost-neutral by 12 months. Design Cost-effectiveness of the two treatment arms will be measured at months 3, 6, 9, and 12. Cost-effectiveness will be calculated using 1.standard cost-effectiveness methodology (incremental cost-effectiveness ratios), and 2.an econometric model to assess multiple outcome measures while controlling for multiple subject and treatment-related factors that are known to affect both outcomes and costs. STUDY OUTCOMES: Total cost for the initial hospitalization of treating stroke subjects randomized to either i.v./i.a. or i.v. rt-PA treatment arms will be measured, as will differences in types of resource utilization over 12 months between the two arms of the trial. Quality-of-life data (EuroQol EQ-5D) will be collected over a 12-month period and quality-adjusted life years will be used as a morbidity-adjusted measure of effectiveness. Subgroup analyses will include dichotomized NIH Stroke Scale (<20, >or=20), country, time between onset and randomization, and i.a. devices.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".