Efficacy of a combination of troxerutin and cerebroprotein hydrolysate in acute cerebral infarction: Meta-analysis and systematic review
Bibliographic record
Abstract
To evaluate the efficacy and safety of combining troxerutin and cerebroprotein hydrolysate (TCH) for treating acute cerebral infarction via a systematic review. The computer-based search encompassed eight databases—PubMed, Cochrane Library, Embase, Web of Science, China Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data, and China Science and Technology Journal Database—from their establishment until December 2023. Randomized controlled trials that assessed TCH for acute cerebral infarction were selected according to inclusion and exclusion criteria. The data extraction, data quality evaluation, and meta-analysis were performed using RevMan 5.4.1 software. The analysis incorporated 18 studies encompassing 1,957 cases. Compared with the control group, the TCH treatment group had superior outcomes in effective rates (risk ratio [RR] = 1.24, 95% confidence interval [CI; 1.18, 1.30], Z = 8.84, p < 0.05), neurological deficit scores (mean difference [MD] = −3.71, 95% CI [−4.32, −3.10], Z = 11.92, p < 0.05), activity of daily living scores (MD = 13.32, 95% CI [11.66, 14.98], Z = 15.75, p < 0.05), changes in low shear viscosity (MD = −1.82, 95% CI [−2.57, −1.06], Z = 4.73, p < 0.05), and plasma fibrinogen levels (MD = −0.43, 95% CI [−0.47, −0.39], Z = 20.01, p < 0.05). However, there was no significant difference in adverse reaction between the two groups (RR = 0.72, 95% CI [0.45, 1.14], Z = 1.39, p = 0.16). No severe adverse drug reactions were observed in either group. Combined TCH is effective and safe for treating acute cerebral infarction.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".