High Blood Pressure and Inflammation Are Associated with Poor Prognosis in Lacunar Infarctions
Bibliographic record
Abstract
UNLABELLED: Lacunar infarction has long been considered to be associated with good prognosis, however a significant percentage of these patients remain functionally dependent. In this study we sought to investigate the factors associated with poor outcome in patients with lacunar infarction. SUBJECTS AND METHODS: We have performed a secondary study in 113 patients with lacunar infarctions admitted within the first 24 h of symptom onset (mean age 70 years, 57.5% men). Blood pressure, body temperature, serum glucose levels, neurotransmitters and pro-inflammatory markers were measured at admission and during the first 72 h. Stroke severity was assessed by the Canadian Stroke Scale (CSS). Neuroimaging evaluation was performed at admission and between days 4 and 7. Poor functional outcome was considered as a Barthel index <85 at 3 months. RESULTS: 36 patients (31.9%) had poor outcome. Older age (p = 0.009), history of hypertension (p = 0.005), higher body temperature (p < 0.0001), systolic blood pressure (SBP) (p = 0.010), serum glucose (p = 0.002) and interleukin-6 (IL-6) (p < 0.0001) levels, as well as lower CSS score at admission (p < 0.0001) were all predictive factors of poor outcome in bivariate analyses. SBP at admission (OR 2.07, CI 95% 1.04-3.28, p = 0.015) was the only clinical predictor on multivariate analysis. When the logistic model was further adjusted for biomarkers of inflammation and excitotoxicity, IL-6 levels (OR 1.09, CI 95% 1.01-1.26, p = 0.003), but not SBP, was independently associated with poor outcome. This association persisted even after adjusting for potential predictors recorded during the first 72 h of hospitalization. CONCLUSION: High SBP and IL-6 levels on admission may predict poor outcome in patients with lacunar infarction.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".