Incidence of Cancer after a Second Unprovoked Venous Thromboembolic Event
Bibliographic record
Abstract
Patients with two unprovoked venous thromboembolism (VTE) events could be at high risk for cancer diagnosis and may therefore benefit from extended cancer screening strategies. However, accurate data on the incidence of cancer in this population is lacking. In a prospective cohort study, we followed-up with all patients who experienced two unprovoked symptomatic VTE events that occurred in less than 2 years apart. We estimated the 1-year incidence rate of cancer following the second unprovoked VTE event using the Kaplan-Meier method. Potential predictors for cancer diagnosis were assessed using a Cox proportional hazard regression model. Between May 2000 and December 2013, we included 197 patients with two episodes of symptomatic unprovoked VTE that occurred in less than 2 years apart. Their mean age was 66.2 ± 16.3 years, and 122 (51.8%) were male. Seventeen patients were diagnosed with cancer during the year following the second episode of unprovoked VTE, corresponding to a cumulative incidence rate of 9.19% (95% confidence interval [CI]: 5.81-14.37). The 1-year cumulative incidence rate of cancer was 35.88% (95% CI: 19.75-59.25) in patients with VTE recurrence on anticoagulation, 5.51% (95% CI: 2.9-10.32) among patients with a second episode of unprovoked VTE occurring after stopping anticoagulation and 1.15% (95% CI: 0.16-7.88) when time elapsed between the first and recurrent VTE was > 1 year. Our study suggests that the incidence of cancer in patients with a second episode of unprovoked VTE that occurs off anticoagulation, or > 1 year after the first event, is similar to that of patients with a first unprovoked VTE event.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".