Treatment with angiotensin receptor blockers before stroke could exert a favourable effect in acute cerebral infarction
Bibliographic record
Abstract
INTRODUCTION: Evidence from experimental and clinical studies is accumulating about the possible cerebral protective properties of antihypertensive drugs, mainly angiotensin receptor blockers (ARB) or angiotensin-converting enzyme inhibitors (ACEI). Our aim was to analyse the impact of prestroke use of antihypertensive drugs on stroke severity and outcome. METHODS: We analysed 1968 consecutive patients with first-ever acute cerebral infarction admitted to an acute stroke unit. Stroke severity was evaluated using the Canadian Neurological Scale and the modified Rankin Score (mRS) was used to evaluate the outcome at discharge. RESULTS: Previous diagnosis of arterial hypertension was reported in 1212 patients and 73% were on antihypertensive treatment. No significant differences in stroke severity were found between patients with or without previous arterial hypertension, either in patients with or without antihypertensive treatment. Patients taking antihypertensive drugs at stroke onset had lower rates of poor outcome than those not on antihypertensive treatment (47 vs. 53%; P = 0.047) and those taking ARB had better outcomes than those without ARB (mRS <or= 2: 75 vs. 65.8%; P = 0.029), with no differences in the analysis of other antihypertensive drugs. The multivariable logistic regression analysis showed that previous treatment with ARB was independently associated with reduced stroke severity (OR: 0.40; 95%CI 0.24-0.65; P < 0.001) and against poor outcome (OR: 0.41; 95%CI 0.23-0.78; P = 0.003). CONCLUSION: Our study suggests that prestroke treatment with ARB may be associated with reduced stroke severity and also with better outcome. This finding agrees with experimental data that suggest a cerebral protective effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".