Reduced Mortality and Severe Disability Rates in the SENTIS Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke trial showed a trend for reduced all-cause mortality and positive secondary safety end point outcomes. We present further analyses of the mortality and severe disability data from the Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke trial. MATERIALS AND METHODS: The Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke trial was a multicenter, randomized, controlled trial that evaluated the safety and effectiveness of the NeuroFlo catheter in patients with stroke. The current analysis was performed on the as-treated population. All-cause and stroke-related mortality rates at 90 days were compared between groups, and logistic regression models were fit to obtain ORs and 95% CIs for the treated versus not-treated groups. We categorized death-associated serious adverse events as neurologic versus non-neurologic events and performed multiple logistic regression analyses. We analyzed severe disability and mortality by outcomes of the mRS. Patient allocation was gathered by use of a poststudy survey. RESULTS: All-cause mortality trended in favor of treated patients (11.5% versus 16.1%; P = .079) and stroke-related mortality was significantly reduced in treated patients (7.5% versus 14.2%; P = .009). Logistic regression analysis for freedom from stroke-related mortality favored treatment (OR, 2.41; 95% CI, 1.22, 4.77; P = .012). Treated patients had numerically fewer neurologic causes of stroke-related deaths (52.9% versus 73.0%; P = .214). Among the 90-day survivors, nominally fewer treated patients were severely disabled (mRS 5) (5.6% versus 7.5%; OR, 1.72; 95% CI, 0.72, 4.14; P = .223). Differences in allocation of care did not account for the reduced mortality rates. CONCLUSIONS: There were consistent reductions in all-cause and stroke-related mortality in the NeuroFlo-treated patients. This reduction in mortality did not result in an increase in severe disability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".