Bibliographic record
Abstract
The potential link between antiplatelet agents and anticoagulants with excess cancer deaths (CD) was reported first for prasugrel (TRITON, DAPT), clopidogrel (DAPT), vorapaxar (TRACER), apixaban (APPRAISE-2), and later ticagrelor (PEGASUS). However, verified CD in the ticagrelor indication-seeking PLATO were not public. We obtained the complete list of deaths and their primary causes in PLATO, matched that dataset against local site records, and analyzed the patterns of CD reporting. The FDA-issued spreadsheet contains 31 precisely detailed CD (PLATO code 12-3). We obtained local site evidence for four CD and matched them with FDA-reported. We also assessed the patterns of how CD were reported among non-vascular death database column “S” by scrolling the FDA Excel file down among 938 PLATO entries. Clopidogrel CD (n = 17) were reported exclusively by sponsor, while independent CRO’s reported only ticagrelor CD (3 out of 14 PLATO total). Among four matched verified outcomes, one ticagrelor CD was correct, second ticagrelor CD was unreported, and two (ticagrelor and clopidogrel) CD were reported inaccurately. Of the remaining 16 clopidogrel CD six were reported as three separate next in line paired entries in Denmark (236–237), Poland (597–598), Romania (679–680), and as two more fatalities in South Africa (786) and Spain (789), while patients 787 and 788 received ticagrelor out of 938 records suggesting possible late addition of incorrect clopidogrel CD reports. We conclude that some CD were misreported in PLATO, favoring ticagrelor. Such mismatch may require reevaluation of this critical outcome in the trial focusing on the exact death cause reported by site investigators.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".