Too good to treat? ischemic stroke patients with small computed tomography perfusion lesions may not benefit from thrombolysis
Bibliographic record
Abstract
OBJECTIVE: Although commonly used in clinical practice, there remains much uncertainty about whether perfusion computed tomography (CTP) should be used to select stroke patients for acute reperfusion therapy. In this study, we tested the hypothesis that a small acute perfusion lesion predicts good clinical outcome regardless of thrombolysis administration. METHODS: We used a prospectively collected cohort of acute ischemic stroke patients being assessed for treatment with IV-alteplase, who had CTP before a treatment decision. Volumetric CTP was retrospectively analyded to identify patients with a small perfusion lesion (<15ml in volume). The primary analysis was excellent 3-month outcome in patients with a small perfusion lesion who were treated with alteplase compared to those who were not treated. RESULTS: Of 1526 patients, 366 had a perfusion lesion <15ml and were clinically eligible for alteplase (212 being treated and 154 not treated). Median acute National Institutes of Health Stroke Scale score was 8 in each group. Of the 366 patients with a small perfusion lesion, 227 (62%) were modified Rankin Scale (mRS) 0 to 1 at day 90. Alteplase-treated patients were less likely to achieve 90-day mRS 0 to 1 (57%) than untreated patients (69%; relative risk [RR] = 0.83; 95% confidence interval [CI], 0.71-0.97; p = 0.022) and did not have different rates of mRS 0 to 2 (72% treated patients vs 77% untreated; RR, 0.93; 95% CI, 0.82-1.95; p = 0.23). INTERPRETATION: This large observational cohort suggests that a portion of ischemic stroke patients clinically eligible for alteplase therapy with a small perfusion lesion have a good natural history and may not benefit from treatment. Ann Neurol 2016;80:286-293.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".