Preoperative medication management turnkey order set for nonemergent adult cardiac surgery
Bibliographic record
Abstract
Objective: The management of preoperative medications is an essential component of perioperative care for the cardiac surgical patient. This turnkey order set is part of a series created by the Enhanced Recovery After Surgery Cardiac Society, first presented at the Annual Meeting of The American Association for Thoracic Surgery in 2023. Numerous guidelines and expert consensus documents have been published to provide guidance in preoperative medication management. Our objective is to integrate these documents into an evidence-based order set that will facilitate standardized implementation of best practices for preoperative medication management for nonemergent adult cardiac surgery. Methods: type. Results: Holding antiplatelet and anticoagulant medications before nonemergent cardiac surgical procedures may reduce the risk of bleeding. Sodium-glucose co-transporter-2 inhibitors and glucagon-like peptide-1 agonists should also be discontinued to prevent acidosis and aspiration, respectively. Specific guidance for frequently used medications are complied within the manuscript, less frequently used medications are listed seperately. Conclusions: Despite strong recommendations from major guidelines and consensus manuscripts, variation exists in preoperative medication orders, with limited availability of succinct implementation tools. This turnkey order set may facilitate standardized comprehensive preoperative medication management before nonemergent cardiac surgery.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.038 |
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".