Bibliographic record
Abstract
PURPOSE OF REVIEW: The indications for aspirin (ASA) for both primary and secondary prevention of thrombotic events continue to evolve. We review some of these indications and the recent literature regarding the perioperative administration of ASA. RECENT FINDINGS: ASA for primary prevention of cardiac ischemia, stroke, cancer, and death remains controversial. When used for primary prevention, ASA may be safely discontinued perioperatively. Patients with coronary or carotid artery stents should continue to receive ASA perioperatively. For patients with ischemic heart disease currently receiving ASA for secondary prevention of cardiac ischemia and stroke undergoing general surgery, orthopedic surgery, ophthalmological surgery, cardiovascular surgery, major vascular surgery, or a urological procedure, continuation of ASA is probably well tolerated, but further study is required. There is no indication to initiate ASA perioperatively in patients with stable ischemic heart disease as the risks outweigh the benefits. Until further data become available, decisions regarding the perioperative continuation of ASA should be made on a case-by-case risk-benefit analysis. SUMMARY: The continuation or discontinuation of ASA perioperatively remains a complicated issue. Further, well designed trials are needed for additional clarification.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".