Abstract WP393: Exploration Of Time-course Combinations Of Outcome Scales For Use In A Global Test Of Stroke Recovery
Bibliographic record
Abstract
Background: Clinical trials for acute ischaemic stroke treatment require large numbers of patients and are expensive to conduct; any method to enhance statistical power or sensitivity is desirable. We explored whether treatment effects may be detected more strongly if outcome is assessed by using combined early and late measures (e.g. 7 day NIH Stroke Scale (NIHSS) combined with 90-day modified Rankin scale (mRS)) than either measure alone. Methods: Data on 4077 patients were available from the Virtual International Stroke Trials Archive (VISTA). We analysed sensitivity to treatment effect, contrasting alteplase as standard care versus non-thrombolysed care. We used proportional odds logistic regression for ordinal scales and Generalised Estimating Equations for global outcomes, with all analyses adjusted for baseline severity and age. We ran simulations to assess relation between sample size and power for each ordinal scale and corresponding global outcomes. We used R 2.12.1 for simulations and SAS 9.2 for all other analyses. Results: All scales were sensitive to treatment effect in isolation. The Table shows ordinal scales and global outcomes of combined early and late outcomes ranked in order of sensitivity to treatment effect, displayed as odds ratio (OR). Discussion: When early and late outcomes were combined in a global test and compared to solitary ordinal scales, any enhancement of the OR appeared marginal. However, this conferred a 20% reduction in required sample size at 80% power. The greatest increment in power arose when component measures were less correlated.
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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 | 0.062 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".