Benefits and risks of resumption of antiplatelet therapy in patients after intracranial hemorrhage: a Meta-analysis
Bibliographic record
Abstract
Objective To assess the benefits and risks of resumption of antiplatelet therapy in patients after intracranial hemorrhage (ICH) by Meta-analysis. Methods Retrieve relevant case-control studies or cohort studies from online databases (January 1, 1990-June 1, 2018) as PubMed, EMBASE/SCOPUS and Cochrane Online Library with key words: intracranial hemorrhages, intracerebral hemorrhages, brain hemorrhages, antiplatelet, restart, resumption. Selection of studies was performed according to pre-designed inclusion and exclusion criteria. Quality of studies was evaluated by using Newcastle-Ottawa Scale (NOS). All data were pooled by RevMan 5.2 software for Meta-analysis. Results The research enrolled 4403 articles, from which 12 high-quality (NOS ≥ 6 scores) studies were chosen after excluding duplicates and those not meeting the inclusion criteria. A total of 4191 cases (1325 cases with resumption of antiplatelet therapy and 2866 cases without resumption of antiplatelet therapy) were included. Meta-analysis showed that comparing with non-resumption of antiplatelet therapy, resumption of antiplatelet therapy was effective in reducing the incidence of ischemic vascular events (RR = 0.700, 95% CI: 0.570-0.850; P = 0.001). There were no significant differences in the risk of ICH recurrence or hematoma expansion (RR = 0.830, 95%CI: 0.580-1.170; P = 0.290) and the incidence of vascular death (RR = 1.300, 95% CI: 0.920-1.840; P = 0.140) between patients with and without resumption of antiplatelet therapy. Conclusions Resumption of antiplatelet therapy in patients after primary ICH effectively reduced the risk of ischemic vascular events, without significant increase of risk of ICH recurrence or hematoma expansion and the occurrence of vascular death. DOI: 10.3969/j.issn.1672-6731.2018.11.005
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.080 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".