The association of antiplatelet agents with mortality among patients with non–COVID-19 community-acquired pneumonia: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Community-acquired pneumonia (CAP) triggers inflammatory and thrombotic host responses driving morbidity and mortality. Antiplatelet agents may favorably modulate these pathways; however, their role in non-COVID-19 CAP remains uncertain. Objectives: To evaluate the association of antiplatelet agents with mortality in hospitalized patients with non-COVID-19 CAP. Methods: We conducted a systematic review and meta-analysis of observational studies and randomized controlled trials (RCTs) of adult patients hospitalized for non-COVID-19 CAP exposed to antiplatelet agents (acetylsalicylic acid or P2Y12 inhibitors). We searched MEDLINE, Embase, and CENTRAL from inception to August 2023. Our primary outcome was all-cause mortality: meta-analyzed (random-effects models) separately for observational studies and RCTs. For observational studies, we used adjusted mortality estimates. Results: = 54%; 2 studies, 225 patients). By the Grading of Recommendations, Assessment, Development, and Evaluation criteria, the certainty of the evidence was low, primarily due to risk of bias. Conclusion: In hospitalized patients with non-COVID-19 CAP, antiplatelet agents may be associated with reduced mortality compared with usual care or placebo, but the certainty of evidence is low.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".