Measuring Outcomes as a Function of Baseline Severity of Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: The spectrum of neurological impairments following acute ischemic stroke is broad. The initial stroke severity predicts responses to treatment and outcomes after ischemic stroke. While clinical trials are using baseline severity as an enrollment criterion or a stratified variable, adjustment of outcome measures as a function of initial impairments has not been done. METHODS: We developed a responder analysis that defines favorable outcomes at 90 days as influenced by the baseline National Institutes of Health Stroke Scale (NIHSS). Favorable outcome was defined as a modified Rankin Scale (mRS) score of 0 if the baseline NIHSS score was <8, mRS score of 0-1 if the NIHSS score was 8-14, and mRS score of 0-2 if the NIHSS score was >14. The concept stemmed from the data of two European rtPA trials. The analysis is a predefined secondary endpoint in a trial testing abciximab. We also used the analysis to reexamine the Trial of Org 10172 in Acute Stroke Treatment data. RESULTS: The responder analysis did not change the overall results of any of the 3 previous trials, but it did give information about differences in responses among subgroups of patients. Evidence about the potential utility of tPA for treatment of patients with mild stroke appeared from the analysis of the second European trial of rtPA. The analysis also provided a hint of efficacy of abciximab. CONCLUSIONS: The responder analysis appears to be a potentially useful way to evaluate outcomes of patients enrolled in clinical trials in stroke. The results of the analysis have clinical relevance and can further explain differences in responses to therapies. In addition, the analysis allows for improved comparisons of results among clinical trials.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".