Patient's Awareness of Cancer-Associated Thrombosis: A Canadian Nationwide Survey
Bibliographic record
Abstract
Background: Approximately 20% of patients with cancer will have cancer-associated venous thromboembolism (CAT), which is associated with significant morbidity and mortality. Despite its clinical importance, CAT awareness in cancer patients and caregivers remains low. We sought to assess the patients' knowledge of CAT through a national survey. Materials and Methods: A survey assessing knowledge of different aspects of CAT was developed by a steering committee including four clinicians with expertise in CAT and a patient partner with lived experience. Survey dissemination among patients with cancer occurred through the Environics network, the Thrombosis Canada member network, the Thrombosis Canada social media platforms, and was advertised through Instagram and Facebook, and the Canadian Cancer Survivor Network newsletter. Results: Out of the 312 patients with cancer or survivors who responded to the survey, 179 (57.4%) were female, and 118 (37.8%) were over 65 years old. Overall, 119 patients (38.1%, 95% confidence interval [CI]: 37.7-49.8%) reported having no knowledge of CAT. Only 84 (26.9%, 95% CI: 22.1-32.2%) and 94 (30.1%, 95% CI: 25.1-35.6%) patients reported receiving education about their underlying risk of CAT or education about signs and symptoms of venous thromboembolism, respectively. A total of 66 (21%, 95% CI: 16.8-26.1%) patients reported being informed by a health care professional about considering thromboprophylaxis. Patients were interested in learning more about the risk of CAT, its associated risk factors, and the benefits and potential side effects of thromboprophylaxis. Conclusion: Many patients with cancer lack awareness or knowledge of CAT. Our results highlight ongoing education and awareness of the CAT burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".