How to undertake procedures while on antiplatelet agents: a hematologist's view
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are the leading cause of mortality globally while also contributing to excess health system costs. Significant advancements have been made in the understanding and prevention of deaths from CVD. In addition to risk factor modifications, one of the key developments in this area is the appropriate prescribing of antiplatelet medications for secondary prevention of CVD. With the advent of vascular devices, there has been an increased use of potent antiplatelet agents to mitigate thrombosis risk. A well-recognized, albeit rare complication of antiplatelet drugs is the heightened risk of bleeding. This adverse effect is particularly relevant when a patient receiving these medications may require an urgent surgery. In addition, for elective surgeries, although these drugs can be withheld, there may be some situations when interruption of antiplatelet agents, even for short duration, may lead to thrombotic events. There are no robust guidelines on how to manage these clinical scenarios, although there have been some important studies published recently in this area. In this review, we provide our approach to patients on antiplatelet drugs who may require urgent surgeries or surgical interventions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".