Bleeding Severity After Percutaneous Coronary Intervention
Bibliographic record
Abstract
Background— In-hospital bleeding after percutaneous coronary intervention is associated with increased mortality. We studied the impact of bleeding severity, defined as magnitude of Hgb (hemoglobin) reduction from baseline (ΔHgb), on the risk of death and other adverse events. Methods and Results— We studied the association between ΔHgb, baseline characteristics, and outcomes among 7608 patients in the ADAPT-DES registry (Assessment of Dual Antiplatelet Therapy With Drug-Eluting Stents) who had information on Hgb values before and after they underwent successful percutaneous coronary intervention. Post-percutaneous coronary intervention, 5985 (78.7%) patients had a drop in Hgb, with 2684 patients (35.3%) having a ΔHgb <1.0 g/dL, 2338 (30.7%) ≥1.0 to <2.0 g/dL, 745 (9.8%) ≥2.0 to <3.0 g/dL, 145 (1.9%) ≥3.0 to <4.0 g/dL, and 73 (1.0%) ≥4.0 g/dL. The risk of dying within 2 years was 3.3% with <1.0 g/dL ΔHgb, 3.4% with ΔHgb ≥1.0 to <2.0 g/dL, 3.7% with ΔHgb ≥2.0 to <3.0 g/dL, 4.1% with ΔHgb ≥3.0 to <4.0 g/dL, and 9.8% with ΔHgb ≥4.0 g/dL ( P =0.03). The risk of major adverse cardiac events (defined as cardiac death, myocardial infarction, or stent thrombosis) was higher for patients with ΔHgb ≥4.0 g/dL (adjusted hazard ratio, 3.39; 95% confidence interval, 1.97–5.83; P <0.001) and for patients with ΔHgb ≥3.0 to <4.0 g/dL (adjusted hazard ratio, 2.17; 95% confidence interval, 1.34–3.53; P =0.002). Conclusions— Among patients who undergo successful percutaneous coronary intervention, bleeding events that result in ΔHgb ≥4.0 g/dL are associated with a considerably increased risk of dying. Clinical Trial Registration— URL: https://www.clinicaltrials.gov . Unique identifier: NCT00638794.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".