Effects of Prior Antiplatelet Therapy on the Prognosis of Primary Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND: Antiplatelet therapy (APT) was prevalently being used in the prevention of vascular disease, but the influence of prior APT on the prognosis of patients with intracerebral hemorrhage (ICH) remains controversial. This meta-analysis was to explore the effects of prior APT on the prognosis of patients with primary ICH. METHODS: PubMed and Embase were searched to identify the eligible studies. The studies comparing the mortality of ICH patients with or without prior APT were included. The quality of these studies was evaluated by the Newcastle-Ottawa quality assessment scale. The adjusted or unadjusted odds ratio (OR) for mortality between ICH patients with and without prior APT were pooled with 95% confidence interval (95% CI) as the effect of this meta-analysis. RESULTS: Twenty-two studies fulfilled the inclusion criteria and exhibited high qualities. The pooled OR was 1.37 (95% CI: 1.13-1.66, P = 0.001) for univariate analysis and 1.41 (95% CI: 1.05-1.90, P = 0.024) for multivariate analysis. The meta-regression indicated that for each 1-day increase in the time of assessment, the adjusted OR for the mortality of APT patients decreased by 0.0049 (95% CI: 0.0006-0.0091, P = 0.026) as compared to non-APT patients. CONCLUSION: Prior APT was associated with high mortality in patients with ICH that might be attributed primarily to its strong effect on early time.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".