Impact of the reporting source on Platelet Inhibition and Treatment Outcomes (PLATO) trial deaths
Bibliographic record
Abstract
BACKGROUND: Platelet Inhibition and Clinical Outcomes (PLATO) was a multicenter, randomized double-blind trial assessing efficacy and safety of ticagrelor versus clopidogrel in patients with acute coronary syndrome. The reported mortality benefit of ticagrelor in the PLATO trial has been challenged for over decade, and never confirmed in later trials. OBJECTIVE: To compare if there were any differences when deaths were reported to the FDAby the sponsors or by independent Contract Research Organizations (CRO). METHODS: We obtained the complete PLATO deaths dataset reported to the FDA and revealed that some events were inaccurately reported favoring ticagrelor. The entire FDA list contains precisely detailed 938 PLATO deaths. The CRO reported outcomes from the USA, Russia, Georgia, and most of Ukraine, while sites in 39 other countries were controlled by the trial sponsors. We compared vascular- (code "11"), non-vascular- (code "12"), and unknown (code "97") deaths triaged by the reporting source. RESULTS: Overall, most PLATO deaths were vascular (n=677), less non-vascular (n=159) andunexpectedly many of "other" (n=7) or "unknown" (n=95) origin reported either by sponsors (n=807) or CRO (n=131). The trial sponsors reported more clopidogrel deaths from vascular (313 vs.239), non-vascular (86 vs.58) and unknown (53 vs. 26) causes.In contrast, CRO-monitored sites reported significantly (72 vs. 53; p<0.01) more ticagrelordeaths than after clopidogrel from vascular (51 vs.39), non-vascular (8 vs.7) and unknown (10 vs. 4) causes. CONCLUSION: Deaths were reported differently by sponsors and CRO within the same trial. Since some deaths were misreported by PLATO sponsors, only the CRO data seems mostly reliable. Among all countries, the CRO - reported PLATO-USA outcomes represent the largest and most realistic dataset of realistic evidence suggesting ticagrelor inferiority to clopidogrel for all primary endpoint components including vascular death.
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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 | 0.124 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".