Nitroglycerin Is Not Associated with Improved Cerebral Perfusion in Acute Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: The study was conducted to test the hypothesis that nitroglycerin (NTG) increases cerebral perfusion focally and globally in acute ischemic stroke patients, using serial perfusion-weighted imaging (PWI) magnetic resonance imaging measurements. PATIENTS AND METHODS: Thirty-five patients underwent PWI immediately before and 72 h after administration of a transdermal NTG patch or no treatment. Patients with baseline mean arterial pressure (MAP) > 100 mmHg (NTG group, n = 20) were treated with transdermal NTG (0.2 mg/h) for 72 h, without a nitrate-free interval. Patients with MAP ≤ 100 mmHg (untreated group, n = 15) were not treated. The primary outcome measure was absolute cerebral blood flow (CBF) in the hypoperfused region at 72 h. RESULTS: The mean baseline absolute CBF in the hypoperfused region was similar in the NTG group (33.3 ± 10.2 ml/100 g/min) and untreated (32.7 ± 8.4 ml/100 g/min, p = 0.4) groups. The median (IQR) baseline infarct volume was 10.4 (2.5-49.3) ml in the NTG group and 32.6 (8.6-96.7) ml in the untreated group (p = 0.09). MAP change in the NTG group was 1.2 ± 12.6 and 8 ± 20.7 mmHg at 2 h and 72 h, respectively. Mean absolute CBF in the hypoperfused region at 72 h was similar in the NTG (29.9 ± 12 ml/100 g/min) and untreated groups (24.1 ± 10 ml/100 g/min, p = 0.8). The median infarct volume increased in untreated (11.8 (5.7-44.2) ml) than the NTG group (3.2 (0.5-16.5) ml; p = 0.033) on univariate analysis, however, there was no difference on regression analysis. CONCLUSION: NTG was not associated with improvement in cerebral perfusion in acute ischemic stroke patients.
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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 | 0.000 | 0.001 |
| 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".