Modified Rankin Scale at 90 Days Versus National Institutes of Health Stroke Scale at 24 Hours as Primary Outcome in Acute Stroke Trials
Bibliographic record
Abstract
BACKGROUND: We investigate whether the National Institutes of Health Stroke Scale (NIHSS) at 24 hours could serve as a primary outcome in acute ischemic stroke trials, and whether combining 90-day modified Rankin Scale (mRS) and 24-hour NIHSS in a hierarchical outcome could enhance detection of treatment effect, using endovascular treatment (EVT) as an exemplary study intervention. METHODS: This was a post hoc analysis of pooled data from 7 randomized EVT trials. Twenty-four-hour NIHSS as a surrogate outcome for 90-day mRS was assessed in a causal mediation model. A 7-point ordinal NIHSS score was generated by grouping 24-hour NIHSS, including death as a separate category ("ordinal" NIHSS). EVT effect sizes and sample sizes required for detecting EVT benefit with 80% power were compared when using granular 24-hour NIHSS, ordinal 24-hour NIHSS, 90-day mRS, and a hierarchical outcome (win ratio) that combines 90-day mRS and 24-hour NIHSS. RESULTS: A total of 1720 patients were included. Twenty-four-hour NIHSS mediated the association between EVT and 90-day mRS and met criteria for a useful surrogate outcome. Effect sizes were highest and sample sizes required to detect EVT benefit smallest for the win ratio approach (228), followed by 90-day mRS (240) and ordinal 24-hour NIHSS (242). In patients with baseline NIHSS <10 and ≥25, ordinal 24-hour NIHSS resulted in the highest effect size. CONCLUSIONS: Twenty-four-hour NIHSS is a useful surrogate outcome for 90-day mRS in patients with acute ischemic stroke undergoing EVT, with a similar EVT effect size compared with 90-day mRS. It could potentially enhance detection of EVT benefit in patients with very low or high baseline NIHSS. An ordered hierarchical outcome could improve detection of EVT treatment effect.
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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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".