The use of anticoagulants for the treatment and prevention of venous thromboembolism in obese patients: implications for safety
Bibliographic record
Abstract
INTRODUCTION: Obesity is a growing problem and is associated with a high risk of venous thromboembolism (VTE). Clinicians are increasingly challenged with prescribing adequate anticoagulants dosing while balancing the risk of bleeding. AREAS COVERED: In this narrative review, we address the safety of unfractionated heparin (UFH), low-molecular-weight heparins (LMWH), fondaparinux, warfarin and direct oral anticoagulants (DOAC) in obese patients. EXPERT OPINION: Obese patients have been under-represented in clinical trials and, therefore, the optimal dosing for both safety and efficacy in this subgroup remains unknown. Current data are based on pharmacokinetic studies in healthy subjects and small-scale cohort studies not adequately powered to detect differences in bleeding or thrombosis. Weight-based dosing of UFH and LMWH should be used over fixed dosing in most obese patients for VTE treatment and prophylaxis. For fondaparinux, increasing the dose with increasing weight is required for VTE treatment and consideration should be given to increasing the dose for VTE prophylaxis. Regarding the DOAC, they should be administered in fixed-dose regimens in the obese sub-population. Given the increasing prevalence of obesity and the associated increased risk of VTE, further studies are needed to establish the safety and efficacy of anticoagulation dosing regimens.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".