Salvianolate injection in the treatment of acute cerebral infarction
Bibliographic record
Abstract
To evaluate the effectiveness and safety of Salvianolate injection (SI) in the treatment of acute cerebral infarction (ACI).We electronically searched databases including PubMed, The Cochrane Library, EMBASE, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, and WanFang Data to collect randomized controlled trials (RCTs) focused on SI treating ACI up to August 2017. Two reviewers independently screened literatures, extracted data, and assessed the risk of bias of included studies. Then, meta-analysis was performed using RevMan 5.3 software.A total of 39 RCTs involving 4516 patients were included. The results of meta-analysis showed that compared with the Western medicine (WM) therapies group [experimental group (EG)], the total effective rate of SI + WM [control group (CG)] was higher (relative risk = 1.29, 95% CI: 1.24-1.35, P < .00001) in 21 RCTs; SI could improve movement function evaluation scores, including National Institute of Health Stroke Scale, Barthel Index, activities of daily living (P < .00001). There was no significant difference in modified Rankin Scale scores between the 2 groups (P = .008) EG was better than CG in improving Montreal Cognitive Assessment scores (P = .001) and Mini-Mental State Examination scores (P < .00001). SI could improved not only the hemorheology indexes, including plasma viscosity, whole blood high-shear viscosity, whole blood low-shear viscosity, fibrinogen (P < .00001), but also high-sensitivity C-reactive protein and C-reactive protein. EG could achieve a better effect on improving the neural deficit scores (P < .00001). There was no significant difference about adverse drug reactions/adverse drug events between the EG and CG (P = .73).Salvianolate can promote recovery of the motor and cognitive function of patients with ACI. However, due to the limited quality and quantity of included studies, more high-quality studies are needed to verify the above conclusion.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".