Physician and Patient Beliefs and Preferences in Pulmonary Embolism and Deep Vein Thrombosis Testing in People with Cancer
Bibliographic record
Abstract
Abstract Introduction It is unclear whether evidence-based diagnostic protocols are followed when cancer patients are tested for venous thromboembolism (VTE). Evidence-based protocols reduce unnecessary diagnostic imaging, offer a patient-centered approach, and have the potential to standardize practice across medical specialties and settings. However, anecdote suggests that specialists who test people with cancer for VTE may prefer diagnostic imaging over clinical probability scoring and D-dimer testing. The aim of this study was to identify physician and patient knowledge, beliefs, values and preferences for VTE testing in cancer. This study was part of a program of research to set International Society of Thrombosis and Haemostasis standards for VTE testing in people with cancer. Methods This was an international qualitative interview study following COREQ guidelines. Semi-structured interviews with physicians and cancer patients were conducted via Zoom. We used purposive sampling to ensure inclusion of physicians from all specialties who test people with cancer for VTE, practicing across all continents. We invited people treated for cancer who had and did not have experience of VTE testing. We used grounded theory to create a conceptual framework which explains physician and patient values and preferences for VTE testing. Transcripts were coded by three researchers independently, who met to discuss their findings and agree on common codes. Researchers were a Thrombosis physician and two undergraduate students who ensured reflexivity was incorporated into their analysis. Results A total of 32 physicians and 6 cancer patients were invited to interview. Of those invited, 23 physicians and 6 patients across 6 continents completed an interview. Interviews lasted between 21 and 86 minutes. Our derived conceptual model can be seen in the attached Figure. Physicians reported a low threshold to test for VTE in people with cancer compared to those without cancer, because VTE was considered a fatal disease and highly prevalent in this patient population. Imaging was generally the only test used for VTE testing in cancer patients. Many participants relied on their Gestalt estimation of VTE probability when deciding whether to order imaging for pulmonary embolism or deep vein thrombosis. Most thought that low Wells score in combination with a negative D-dimer was not sufficiently sensitive to exclude VTE and anticipated the Wells score and D-dimer to be elevated. The Wells scores had poor face validity because they do not include cancer-specific variables and participants hoped to see a more nuanced formal score for VTE testing in cancer patients. Participants believed that their colleagues would support their diagnostic approach. Patients reported they were used to having tests and CT scans. Patients felt it was important for their physicians to prioritize testing for VTE. Patients had full trust and confidence in their physicians' testing decisions, particularly in decisions made by their oncologists. Conclusion Physicians have a low threshold to test people with cancer for VTE and tend not to use clinical probability assessment and D-dimer. Patients are comfortable having diagnostic imaging, feel VTE testing is important and have full trust in their physicians. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".