The FDA and PLATO investigators death lists: Call for a match
Bibliographic record
Abstract
Purpose: The FDA-issued PLATO trial dataset revealed that some primary deaths causes (PDC) were inaccurately reported favoring ticagrelor. However, the PLATO Investigators operated the shorter death list of uncertain quality. We compared if PDC match when trial fatalities were reported to the FDA and by the PLATO investigators. Method: The FDA list contains precisely detailed 938 PLATO deaths, while shorter investigators dataset consists of 905 deaths. We matched 4 vascular (sudden, post-MI, heart failure and stroke), and 3 non-vascular (cancer, sepsis and suicide) PDC between death lists. Results: There were more sudden deaths in the shorter list than in the FDA dataset (161 vs.138; p<0.03), post-AMI (373 vs.178; p<0.001) but fewer heart failure deaths (73 vs.109; p=0.02). Stroke numbers match well (39 vs. 37; p=NS) with only 2 ticagrelor cases removed. Cancer matched well (32 vs.31; p=NS), and sepsis cases were identical (30 vs. 30; P=NS). However, 2 extra clopidogrel suicides in the shorter list are impossible to comprehend. Conclusions: The PLATO trial PDC were mismatched between FDA and Investigators sets. We are kindly asking the ticagrelor sponsor or/and concerned PLATO Investigators to clarify the PDC dataset match.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.244 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".