Implementation failure: thromboprophylaxis in ambulatory patients with cancer
Bibliographic record
Abstract
Venous thromboembolism (VTE) is a frequent complication in patients with cancer, especially those receiving systemic therapy in the ambulatory setting. Despite being a largely preventable condition, it remains a leading cause of morbidity and mortality in this patient population. Risk prediction models, such as the Khorana score, have been developed to stratify patients according to their underlying risk of VTE and identify those most likely to benefit from thromboprophylaxis by improving its risk-benefit ratio. Recent evidence supports the efficacy and safety of both low molecular weight heparin and direct oral anticoagulants in reducing VTE incidence in ambulatory patients with cancer who are receiving systemic therapy and are at high risk of VTE. Nevertheless, despite guideline recommendations warranting a risk-based approach, studies persistently show low adoption of thromboprophylaxis in this patient population. Barriers to implementation are complex, including clinician-, patient- and system-related factors. However, promising implementation strategies, including electronic health record integrated risk calculators, structured education programs, and patient-centered care pathways, have shown potential in improving adherence to guidelines and better clinical outcomes. This review summarizes the current evidence for thromboprophylaxis in patients with cancer, explores the challenges in translating evidence into practice, and highlights successful models designed to close the gap between guidelines and clinical practice.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".