Anticoagulation in type 2 myocardial infarctions: Lessons learned from the rivaroxaban in type 2 myocardial infarctions feasibility trial
Bibliographic record
Abstract
Background: Type 2 myocardial infarction (T2MI) occurs when myocardial oxygen demand exceeds myocardial oxygen supply. T2MIs occur more frequently and have worse outcomes compared to Type 1 myocardial infarction caused by an acute plaque rupture. No clinical trial evidence is available to guide pharmacological therapies in this high-risk population. Methods: The Rivaroxaban in Type 2 Myocardial Infarction (R2MI) trial (NCT04838808) was a trainee-led, pragmatic, pilot study that randomised patients with a T2MI to either rivaroxaban 2.5 mg twice daily or placebo. The trial was stopped early due to low recruitment. Investigators explored the challenges of conducting the trial in this population. This was supplemented by a retrospective chart review of 10,000 consecutive troponin assays undertaken during the study period. Results: Over a 1-year period, 276 patients with T2MI were screened for inclusion of which only 7 (2.5%) were randomised in the trial. Study investigators identified trial design and participant population factors that limited recruitment. These included: heterogeneity of patient presentation, poor clinical prognosis, and lack of dedicated non-trainee study personnel. The major limitation to recruitment was the frequency of identified exclusion criterion. The retrospective chart review identified 1715 patients with an elevated high-sensitivity troponin level, of which 916 (53%) were adjudicated to be related to T2MI. Of these, 94.5% possessed an exclusion criterion for the trial. Conclusion: Patients with a T2MI are challenging to recruit into clinical trials involving oral anticoagulation. Future studies should account for only ∼1 in every 20 screened individuals being a candidate for study recruitment.
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 | 0.055 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".