Bibliographic record
Abstract
Abstract Background Cardiovascular benefits of aggressive dual antiplatelet therapy may be associated with extra risks including bleeding, cancer, and infections discovered first for prasugrel in the TRial to assess Improvement in Therapeutic Outcomes by optimizing platelet InhibitioN with prasugrel (TRITON) trial. Ticagrelor in PLATO also caused slightly more infections but surprisingly less sepsis-related deaths (SRD) than clopidogrel. However, verified infection fatalities in PLATO were lacking from the public domain. We obtained the complete Food and Drug Administration (FDA)-issued primary causes death list, matched it with the few local site records dataset and analyzed the patterns of infections and deaths reported in PLATO. Methods Among infections, the FDA spreadsheet contains only two primary death codes for pneumonia (12–2) and SRD (12–8). We obtained local evidence for two pneumonia and two SRD and matched those with the FDA records. We assessed how SRD patterns were reported among nonvascular death's dataset. Results The FDA PLATO records indicate that clopidogrel caused numerically less (n = 8) primary pneumonia deaths than ticagrelor (n = 10) but over three times more SRD (n = 23/7). Among matched verifiable outcomes, both pneumonia deaths were correct, but two clopidogrel SRD were incorrect. Of the remaining 21 clopidogrel SRD, 6 were reported as two separate closed paired entries in Brazil (lines 76 and 78 and 86 and 88) and India (lines 436 and 440), suggesting last minute addition of potentially incorrect SRD reports. Four ticagrelor SRD (lines 24,193,467 and 650) were “compensated” with close or next in line clopidogrel SRD entries (lines 22,195,468 and 651). Conclusion The FDA-issued evidence suggests no benefit of ticagrelor in preventing deaths from infections with slightly more pneumonia deaths, with possible misreporting of SRD in PLATO. These findings require an in-depth precise review of sepsis deaths in this trial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".