Assessing the risk of venous thromboembolism (VTE) in ambulatory patients with cancer: Rationale and implementation of a pharmacist-led VTE risk assessment program in an ambulatory cancer centre
Bibliographic record
Abstract
Objectives The objectives of this paper were to identify and compare clinical prediction models used to assess the risk of venous thromboembolism (VTE) in ambulatory patients with cancer, as well as review the rationale and implementation of a pharmacist-led VTE screening program using the Khorana Risk Score model in an ambulatory oncology centre in Sault Ste. Marie, Ontario, Canada. Data Sources PubMed was used to identify clinical practice guidelines and review articles discussing risk prediction models used to assess VTE risk in ambulatory patients with cancer. Data Summary Three commonly used VTE risk prediction models in ambulatory patients with cancer: the Khorana Risk Score, Vienna Cancer and Thrombosis Study (CATS) and Protecht Score, were identified via literature review. After considering guideline recommendations, site-specific factors (i.e. laboratory costs, time pharmacists spent calculating VTE risk) and evidence from the CASSINI and AVERT trials, a novel pharmacist-led VTE risk assessment program using the Khorana Risk Score was developed during a fourth-year PharmD clinical rotation at the Algoma District Cancer Program (ADCP) [ambulatory cancer care centre]. ADCP patients with a Khorana Risk Score of [Formula: see text] were referred to the hematologist for a full VTE workup. Considering limitations, inclusion and exclusion criteria of the CASSINI and AVERT trials, the hematologist and pharmacy team decided on appropriate initiation of thromboprophylaxis with a direct oral anticoagulant (DOAC). Conclusions The Khorana Risk Score was the chosen model used for the pharmacist-led VTE risk assessment program due to its user-friendly scoring algorithm, evidence from validation studies and clinical trials, as well as ease of integration into pharmacy workflow. More research is needed to determine if pharmacist-led VTE risk assessment programs will impact patient outcomes, such as morbidity and mortality, secondary to cancer-associated thrombosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".