Awareness of venous thromboembolism among patients with cancer: Preliminary findings from a global initiative for World Thrombosis Day
Bibliographic record
Abstract
BACKGROUND: Cancer-associated venous thromboembolism (CAT) has detrimental impact on patients' clinical outcomes and quality of life. Data on CAT education, communication, and awareness among the general cancer population are scanty. METHODS: We present the preliminary results of an ongoing patient-centered survey including 27 items covering major spheres of CAT. The survey, available in 14 languages, was promoted and disseminated online through social networks, email newsletters, websites, and media. RESULTS: As of September 20, 2022, 749 participants from 27 countries completed the survey. Overall, 61.8% (n = 460) of responders were not aware of their risk of CAT. Among those who received information on CAT, 26.2% (n = 56) were informed only at the time of CAT diagnosis. Over two thirds (69.1%, n = 501) of participants received no education on signs and symptoms of venous thromboembolism (VTE); among those who were educated about the possible clinical manifestations, 58.9% (n = 119) were given instructions to seek consultation in case of VTE suspicion. Two hundred twenty-four respondents (30.9%) had a chance to discuss the potential use of primary thromboprophylaxis with health-care providers. Just over half (58.7%, n = 309) were unaware of the risks of bleeding associated with anticoagulation, despite being involved in anticoagulant-related discussions or exposed to anticoagulants. Most responders (85%, n = 612) valued receiving CAT education as highly relevant; however, 51.7% (n = 375) expressed concerns about insufficient time spent and clarity of education received. CONCLUSIONS: This ongoing survey involving cancer patients with diverse ethnic, cultural, and geographical backgrounds highlights important patient knowledge gaps. These findings warrant urgent interventions to improve education and awareness, and reduce 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".