Outcomes with P2Y12 inhibitor monotherapy after PCI according to bleeding risk: A Bayesian meta-analysis
Bibliographic record
Abstract
BACKGROUND: P2Y12 inhibitor monotherapy is a promising novel strategy to reduce bleeding complications compared to dual antiplatelet therapy (DAPT) in patients undergoing percutaneous coronary intervention (PCI). In order to personalise treatment with DAPT based on patients' bleeding risk, we compared outcomes after PCI between P2Y12 inhibitor monotherapy and DAPT according to bleeding risk. METHODS: A search for randomized clinical trials (RCTs) comparing P2Y12 inhibitor monotherapy after a short period of DAPT to standard DAPT after PCI was performed. Outcome differences between treatment groups regarding major bleedings, major adverse cardiac and cerebral events (MACCE) and net adverse clinical events (NACE) were assessed with hazard ratios (HRs) and corresponding credible intervals (CrI) according a Bayesian random effects model in patients with and without high bleeding risk (HBR). RESULTS: Five RCTs including 30,084 patients were selected. P2Y12 inhibitor monotherapy compared to DAPT reduced major bleedings in the total population (HR: 0.65, 95 % CrI: 0.44 to 0.92). The HRs of the HBR and non-HBR subgroups showed a similar reduction of bleedings for monotherapy (HBR: HR 0.66, 95 % CrI: 0.25 to 1.74; non-HBR: HR 0.63, 95 % CrI: 0.36 to 1.09). No notable differences between treatments on MACCE and NACE were observed in either sub-group or in the total population. CONCLUSIONS: Regardless of bleeding risk, P2Y12 inhibitor monotherapy is the favourable choice after PCI regarding major bleedings and does not increase ischemic events compared to DAPT. This suggests that bleeding risk is not decisive when considering P2Y12 inhibitor monotherapy.
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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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".