Effect Size Estimates for the ESCAPE Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Ordinal outcomes, such as modified Rankin Scale (mRS), are the standard primary end points in acute stroke trials. Regression models for assessing treatment efficacy after adjusting for baseline covariates have been developed for continuous, binary, or ordinal end points. There has been no consensus on the best choice of method for analyzing these data. METHODS: We compared several regression models for assessing treatment efficacy in acute stroke trials using existing data sets from the Interventional Management of Stroke-III and Prolyse in Acute Cerebral Thromboembolism II (PROACT-2) trials. Patients with baseline non-contrast computed tomographic Alberta Stroke Program Early CT Score (ASPECTS) > 5, baseline computed tomographic angiography, or conventional angiogram showing an intracranial internal carotid artery or middle cerebral artery trunk (M-1) occlusion, adequate collateral circulation shown on computed tomographic angiography, and treatment times of non-contrast computed tomographic to groin puncture of ≤90 minutes, were included. Monte Carlo techniques were used to compare the statistical power of these regression models under a variety of simulated data analytic scenarios. RESULTS: Binary logistic regression showed greater power when the treatment is predicted to show evidence of benefit on one end of the mRS with no other gains across other levels of the scale. Proportional odds regression showed greater power when the treatment is predicted to show evidence of improvement on both ends of the mRS. CONCLUSIONS: The mRS distribution for both treatment and control groups influences the power of the investigated statistical models to assess treatment efficacy. A careful evaluation of the expected outcome distribution across the mRS scale is required to determine the best choice of primary analysis.
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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 | 0.164 | 0.343 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".