Bleeding and New Cancer Diagnosis in Patients With Atherosclerosis
Bibliographic record
Abstract
BACKGROUND: Patients treated with antithrombotic drugs are at risk of bleeding. Bleeding may be the first manifestation of underlying cancer. METHODS: We examined new cancers diagnosed in relation to gastrointestinal or genitourinary bleeding among patients enrolled in the COMPASS trial (Cardiovascular Outcomes for People Using Anticoagulation Strategies) and determined the hazard of new cancer diagnosis after bleeding at these sites. RESULTS: Of 27 395 patients enrolled (mean age, 68 years; women, 21%), 2678 (9.8%) experienced any (major or minor) bleeding, 713 (2.6%) experienced major bleeding, and 1084 (4.0%) were diagnosed with cancer during a mean follow-up of 23 months. Among 2678 who experienced bleeding, 257 (9.9%) were subsequently diagnosed with cancer. Gastrointestinal bleeding was associated with a 20-fold higher hazard of new gastrointestinal cancer diagnosis (7.4% versus 0.5%; hazard ratio [HR], 20.6 [95% CI, 15.2-27.8]) and 1.7-fold higher hazard of new nongastrointestinal cancer diagnosis (3.8% versus 3.1%; HR, 1.70 [95% CI, 1.20-2.40]). Genitourinary bleeding was associated with a 32-fold higher hazard of new genitourinary cancer diagnosis (15.8% versus 0.8%; HR, 32.5 [95% CI, 24.7-42.9]), and urinary bleeding was associated with a 98-fold higher hazard of new urinary cancer diagnosis (14.2% versus 0.2%; HR, 98.5; 95% CI, 68.0-142.7). Nongastrointestinal, nongenitourinary bleeding was associated with a 3-fold higher hazard of nongastrointestinal, nongenitourinary cancers (4.4% versus 1.9%; HR, 3.02 [95% CI, 2.32-3.91]). CONCLUSIONS: In patients with atherosclerosis treated with antithrombotic drugs, any gastrointestinal or genitourinary bleeding was associated with higher rates of new cancer diagnosis. Any gastrointestinal or genitourinary bleeding should prompt investigation for cancers at these sites. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01776424.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".