Prior antiplatelet therapy and outcome following intracerebral hemorrhage A systematic review
Bibliographic record
Abstract
Objectives: Antiplatelet therapy (APT) promotes bleeding; therefore, APT might worsen outcome in patients with intracerebral hemorrhage (ICH). We performed a systematic review and meta-analysis to address the hypothesis that pre-ICH APT use is associated with mortality and poor functional outcome following ICH. Methods: The Medline and Embase databases were searched in February 2008 using relevant key words, limited to human studies in the English language. Cohort studies of consecutive patients with ICH reporting mortality or functional outcome according to pre-ICH APT use were identified. Of 2,873 studies screened, 10 were judged to meet inclusion criteria by consensus of 2 authors. Additionally, we solicited unpublished data from all authors of cohort studies with >100 patients published within the last 10 years, and received data from 15 more studies. Univariate and multivariable-adjusted odds ratios (ORs) for mortality and poor functional outcome were abstracted as available and pooled using a random effects model. Results: We obtained mortality data from 25 cohorts (15 unpublished) and functional outcome data from 21 cohorts (14 unpublished). Pre-ICH APT users had increased mortality in both univariate (OR 1.41, 95% confidence interval [CI] 1.21 to 1.64) and multivariable-adjusted (OR 1.27, 95% CI 1.10 to 1.47) pooled analyses. By contrast, the pooled OR for poor functional outcome was no longer significant when using multivariable-adjusted estimates (univariate OR 1.29, 95% CI 1.09 to 1.53; multivariable-adjusted OR 1.10, 95% CI 0.93 to 1.29). Conclusions: In cohort studies, APT use at the time of ICH compared to no APT use was independently associated with increased mortality but not with poor functional outcome. Neurology (R) 2010;75:1333-1342
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".