Importance of proper patient selection and endpoint selection in evaluation of new therapies in acute stroke: further analysis of the SENTIS trial
Bibliographic record
Abstract
BACKGROUND: The magnitude of treatment effect in acute stroke depends on several factors, including time from symptom onset (TFSO) to treatment and severity of the initial insult. OBJECTIVE: To report further evaluation of NeuroFlo therapy, focusing on the effect of time and stroke severity. METHODS: SENTIS was a prospective randomized trial (N=515) comparing standard medical therapy with/without NeuroFlo therapy. For this analysis, we evaluated outcomes in groups of patients based on TFSO and stroke severity: patients randomized <6 h, 6-10 h, and >10 h with mild (NIHSS<8), moderate (8-14), and severe (>14) symptoms at randomization. 90-Day mRS (modified Rankin Scale) scores and stroke-related death rates were compared between treatment groups. RESULTS: For patients randomized <6 h TFSO (n=128), the OR for mRS 0-2 was 3.11 (CI 1.30 to 7.46, p=0.011) for treated versus non-treated patients. In patients with disease of moderate severity (NIHSS 8-14, n=214), NeuroFlo-treated patients were more likely to have a good outcome (mRS 0-2; OR=1.84, CI 1.02 to 3.33, p=0.043). The stroke-related death rate was better in the treated group with TFSO >10 h and NIHSS >14 (n=42) (OR=7.10, CI 1.13 to 44.55, p=0.036). CONCLUSIONS: The results of our analysis support the importance of careful selection of outcome measures and the impact that rapid treatment and initial stroke severity have on outcome.
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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 | 0.121 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".