Neurologic safety event rates in the SENTIS trial control population
Bibliographic record
Abstract
BACKGROUND: Adverse event (AE) rates for interventional stroke trials are not well established. AIMS: We prospectively evaluated control arm AEs from a randomized stroke trial to establish expected rates of neurologic AEs. METHODS: Control data from the Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke (SENTIS) Trial were evaluated. Patients were ≥ 18 years with National Institutes of Health Stroke Scale (NIHSS) scores 5-18 within 14 h of stroke onset. Follow-up was 90 days. Neurological AEs and serious AEs (SAEs) were adjudicated and the following defined times used to determine treatment relatedness: 24-h imaging for intracranial hemorrhage (ICnH) including hemorrhagic transformation, 7 days each for cerebral edema and neurologic worsening/stroke progression, and 30 days for new ischemic strokes. RESULTS: The control group included 257 patients, 49.4% female, mean age of 68.3 years, and median NIHSS of 10. Neurologic AEs occurred at the following rates: ICnH 27.6%, cerebral edema 6.6%, neurologic worsening 18.3%, and new stroke 4.7%. Most of these events occurred within the defined times: ICnH 74.6%, cerebral edema 94.1%, neurologic worsening 87.2%, and new stroke 83.3%. CONCLUSIONS: SENTIS Trial control arm neurologic events provide estimates of expected AE rates and defined times that can be used for future stroke trial's safety assessments.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".