Inflammatory markers in progressing lacunar stroke
Bibliographic record
Abstract
89 Background: The mechanisms underlying neurologic deterioration in patients with lacunar infarction have not been established. While accumulating evidence suggest a role for inflammatory-mediated damage after brain ischemia, whether inflammation also intervenes in lacunar stroke progression remains unsettled. Objective: We sought to investigate the role of inflammatory markers in the progression of lacunar infarctions Methods: We studied within the first 24 hours after the onset of symptoms 113 consecutive patients (mean age 69.7±9.3, 57% males) with lacunar stroke defined by clinical and CT/MRI criteria (lacunar syndrome + lesion of < 15mm in appropriate location). Neurological deterioration was defined as a fall of 1 or more points in Canadian Stroke Scale (CSS) between inclusion and 48 hours. Interleukine 6 (IL-6), tumor necrosis factor-α (TNF-α) and ICAM-1 were determined by ELISA in blood samples obtained on admission. Results: At follow up, 27 patients (23.9%) progressed on the CSS. Among risk factors and clinical characteristics, only a history of hypertension, and a lack of prior treatment with aspirin, were significantly more frequent in patients with neurologic deterioration (p<0.05). Serum TNF-α (17.3±5.02 vs 8.7±4.7 pg/mL), IL-6 (29.2±7.2 vs 13.7±7.5 pg/mL) and ICAM-1 (270.6±65.0 vs 160.6±29.8 pg/mL) were significantly higher in patients who progressed (p<0.001). Logistic regression analyses adjusted for history of hypertension and prior aspirin intake showed that TNF−α [OR=1.36, 95% CI (1.20–1.53)], IL-6 [OR=1.25, 95% CI (1.14–1.36)] and ICAM-1 [by 10 pg/mL, OR=2.85, 95% CI (2.79–2.91)] were related to lacunar stroke progression. Conclusions: Our results suggest that inflammation contributes to the progression of lacunar infarctions. Further studies will establish the mechanisms underlying this provocative finding that opens a new therapeutic avenue in lacunar stroke.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".