10.1714/0000.42096
Bibliographic record
Abstract
BACKGROUND: Cancer is an important condition associated with the development of atrial fibrillation (AF). The objectives of the BLITZ-AF Cancer study were to collect real-life information on the clinical profile and use of antithrombotic drugs in patients with AF and cancer to improve clinical management, as well as the evaluation of the association between different antithrombotic treatments (or their absence) and the main clinical events. METHODS: European multinational, multicenter, prospective, non-interventional study conducted in patients with AF (electrocardiographically confirmed) and cancer occurring within 3 years. The CHA2DS2-VASc and the HAS-BLED scores were calculated in all enrolled patients. RESULTS: From June 2019 to July 2021, 1514 patients were enrolled, 36.5% women, from 112 cardiology departments in 6 European countries (Italy, Belgium, the Netherlands, Spain, Portugal and Ireland). Italy enrolled 971 patients in 77 centers. Average age of patients was 74 ± 9 years, of which 20.9% affected by heart failure, 18.1% by ischemic heart disease, 9.8% by peripheral arterial disease and 38.5% by valvular diseases; 41.5% of patients had a CHA2DS2-VASc score ≥4. The most represented cancer sites were lung (14.9%), colorectal tract (14.1%), prostate (8.8%), or non-Hodgkin's lymphoma (8.1%). Before enrollment, 16.6% of patients were not taking antithrombotic therapy, while 22.7% were on therapy with antiplatelet agents and/or low molecular weight heparin. After enrollment these percentages decreased to 7.7% and 16.6%, respectively and, at the same time, the percentage of patients on direct oral anticoagulant (DOAC) therapy increased from 48.4% to 68.4%, also to the detriment of those on vitamin K antagonist therapy. CONCLUSIONS: The BLITZ-AF Cancer study, which enrolled patients diagnosed with AF and cancer, highlights that the use of DOACs by cardiologists in this clinical context has increased, even though the guidelines on AF do not give accurate indications about oral anticoagulant therapy in patients with cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.953 | 0.935 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".