Effectiveness of t-PA in acute ischemic stroke
Bibliographic record
Abstract
OBJECTIVE: To examine whether the demonstrated efficacy of tissue-type plasminogen activator (t-PA) for acute ischemic stroke can be effective in a community setting. METHODS: Sixty-eight consecutive patients with acute ischemic stroke treated with IV t-PA within 3 hours of symptom onset by attending general neurologists in a busy teaching hospital. Outcome measures at 3 months were the National Institute of Health Stroke Scale (NIHSS), functional outcome (independence [modified Rankin score 0-2], dependence [modified Rankin score 3-5], and death), and symptomatic hemorrhage. Appropriately treated patients were defined by adherence to the National Institute of Neurological Disorders and Stroke (NINDS) guidelines. Effectiveness is expressed as the absolute risk reduction in which the baseline risk is assumed to be similar to that of the NINDS control group. RESULTS: Of 68 consecutively treated patients (with a mean baseline NIHSS score of 15 +/- 6), 26 (38%) made a full recovery and 39 (57%) made an independent recovery. The 11 patients who violated protocol had a lower probability of independence (p < 0.02) and full neurologic recovery (p < 0.02) and a higher probability of symptomatic hemorrhage (p < 0.05) and death (p < 0.01) compared with those of 57 patients treated according to NINDS guidelines. CONCLUSIONS: The use of t-PA for stroke in this community is effective with a number needed to treat of six. The risk of symptomatic hemorrhage is similar to that noted in randomized trials. Treating patients who violate protocol results in excess risk with no observable benefit.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".