Potential effect of green tea extract for adjuvant treatment of acute ischemic stroke by s100ß upregulation in non-thrombolysis patient
Bibliographic record
Abstract
Abstract Purpose To determine the efficacy of green tea extract for adjuvant treatment of acute ischemic stroke in non-thrombolysis patients. Methods A double-blind, randomized controlled trial was conducted in January-November 2021. The subjects were all acute ischemic stroke patients who presented to the ER during recruitment. Patients were then randomized into control or intervention groups and followed up to 30 days of treatment. On days 0,7,14, and 30 of the treatment, NIHSS, mRS and MoCAIna scores were measured. IL-10 and S100ß were measured on the first and seventh days of admission. Results All the delta-NIHSS (day 7 − 0, day 14 − 0, day 30 − 0) were found to be statistically significant in the intervention group (p = 0.019, p = 0.002 and p < 0.001, respectively), especially the NIHSS improvement on day 30 − 0 (RR 4,333 (1,162–16,157); CI 95%; p = 0.014). On day 30, the mRS score was significantly higher in the intervention group compared to the control group (1.00 (0.00–4.00) vs 2.00 (1.00–4.00); p = 0.046). The delta-mRS day 14 − 0 and 30 − 0 were statistically significant for the intervention group (p = 0.042 and p = 0.001). There is no significant difference between the two groups in the MoCAIna score and IL-10 level. On days 0 and 7, the S100ß were found to be statistically significant in the intervention group (p = 0.041 and p = 0.013). The delta- S100β day 7 − 0 was found to be significantly lower in the intervention groups compared to the control group (-1.388 (-4.420–5.010) 0.900 (-1.800–5.660); p-value < 0.001). Conclusions The green tea extract with EGCG through up-regulation S100ß can improve the clinical outcomes of acute cerebral infarction 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".