Primary Causes of Death Reported to the FDA Suggest Less Ticagrelor Mortality Benefit than the List Issued to the PLATO Trial Investigators
Bibliographic record
Abstract
Background: The PLATO trial data set reported to the FDA (DRFDA) revealed that some primary deaths causes (PDC) were inaccurately reported favoring ticagrelor. Trial Investigators (DRTI) received different data set with more ticagrelor mortality advantage. We compared these two death lists for the match in PDC. Methods and Results: The DRFDA contains 938 deaths, while the DRTI contains 905. We matched “vascular”, “non-vascular”, “unknown”, “missed”, and “other” causes of death between DRFDA and DRTI. The DRFDA used 14 vascular, 9 non-vascular, 1 unknown and 1 other PDC codes, while the DRTI used 14 but different vascular, 14 non-vascular but no unknown or other PDC codes. We observed a significant mismatch for the PDC codes between the DRFDA and DRTI data sets. Most DRFDA deaths were vascular (n = 677), fewer non-vascular (n = 159) and unexpectedly many unknown (n = 95) or other (n = 7) PDC. Surprisingly, the shorter DRTI contains more vascular (n = 795), fewer non-vascular (n = 110), but no unknown, other, or missed causes. There were more sudden deaths in DRTI than in DRFDA (161 vs. 138; p < 0.03), twice as many post-myocardial infarction deaths (373 vs. 178; p < 0.001) but fewer heart failure deaths (73 vs. 109; p = 0.02). The reported non-vascular PDC match better except for 2 extra suicides in the clopidogrel arm of the DRTI. Conclusions: Over 100 “unknown”, “missed”, or “other” PDC events reported by the trial sponsor to the FDA were omitted from the investigator data set contributing to the inflated differences in vascular mortality benefit of ticagrelor reported in numerous PLATO publications. Synchronization of PDC reporting between regulatory agencies and investigators was lacking in PLATO but remains mandatory to ensure quality for future indication-seeking trials.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".