The Effect of Perioperative Aspirin Therapy in Peripheral Vascular Surgery: A Decision Analysis
Bibliographic record
Abstract
Patients who undergo infrainguinal revascularization surgery are at increased risk for perioperative thrombotic complications. Aspirin decreases thrombotic events in the nonoperative setting; however, aspirin is often discontinued to avoid perioperative hemorrhagic complications. We used a decision analysis to determine whether aspirin should be discontinued before infrainguinal revascularization surgery. Two strategies were compared: aspirin cessation 2 wk before surgery and aspirin continuation throughout the perioperative period. Clinical events examined included myocardial infarction, thrombotic cerebrovascular accident, hemorrhagic cerebrovascular accident, gastrointestinal hemorrhage, and incisional hemorrhagic complications. Event rates and effect of aspirin were obtained by using MEDLINE. The outcomes were perioperative mortality, life expectancy, and quality-adjusted life expectancy. According to the model, continued aspirin use decreased perioperative mortality rates from 2.78% to 2.05%. Continued aspirin use increased life expectancy from 14.83 to 14.89 yr and increased quality-adjusted life expectancy from 14.72 to 14.79 yr. Aspirin increased the number of hemorrhagic complications by 2.46%, primarily because of an increased incidence of non-life-threatening complications.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".