Managing oral anticoagulant therapy in patients with mechanical heart valves undergoing elective surgery
Bibliographic record
Abstract
The optimal perioperative anticoagulation management in patients on warfarin therapy is poorly defined because of the lack of randomized trials. Because guidelines are heterogeneous, we hypothesized that treatment strategies are not uniform in clinical practice. During the Annual Meeting of the Italian Federation of Anticoagulation Clinics, a questionnaire with four different clinical scenarios was distributed. Two scenarios described the cases of patients with a mechanical heart valve in the mitral position and additional risk factors for systemic embolism, one undergoing major surgery (case 1) and one undergoing minor surgery (case 3). Two scenarios described patients with an aortic mechanical heart valve, one undergoing major surgery (case 2) and one minor surgery (case 4). Different preoperative and postoperative management options were offered. Treatment options for all scenarios were the same. Of the 300 questionnaires distributed, 72 were returned (24%). Outpatient full-dose low molecular weight heparin (LMWH) was the most selected strategy in the preoperative phase. It was chosen by 90.3% of participants in case 3, 87.5% in case 1, 62.5% in case 2, and 52.8% in case 4. In the postoperative phase, full-dose LMWH remained the most selected option for cases 1 (75%) and 3 (70.8%), whereas low-dose LMWH was selected by the majority for cases 2 (50%) and 4 (61.1%). Even among expert clinicians, the management of perioperative anticoagulation is heterogeneous. In particular, the definition of risk categories and the optimal intensity of antithrombotic drugs remain to be assessed by well-designed prospective studies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".