The Risk of Malignancy Following Idiopathic Venous Thromboembolism: A Population-Based Cohort Study
Bibliographic record
Abstract
Abstract Abstract 5255 Background: Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary embolism (PE), can be the earliest manifestation of an occult malignancy. Objectives: We used the hospital discharge (MED ECHO) database of the province of Quebec, Canada to determine the risk of a new cancer diagnosis in patients with idiopathic VTE. Methods: Using data from Med-Echo which systematically records information on all hospital admissions in Quebec, we constructed a cohort of all individuals with a first-time diagnosis of DVT or PE between January 1, 1996 and December 31, 2004 and no cancer diagnosis preceding the VTE. Subjects were excluded if there was a surgical procedure, pregnancy, trauma or hospitalization in the 3 months prior to the VTE. Subjects were followed for 12 months after VTE for a first diagnosis of cancer (except non-melanoma skin cancer). VTE and cancer were defined using the 9th edition International Classification of Diseases codes. Results: In all, 20 740 patients with idiopathic VTE and without previous cancer were identified. The mean age was 65.9 years (SD 12.6) and 58% were female. Overall, 696 (3.4%) patients were diagnosed with cancer in the 12 months following VTE. The risk was lowest among subjects aged 50 years or less (1.0%) and highest among patients aged 80 years or more (5.5%). Lung (25%) and gastrointestinal tumours (21%) were the most common diagnoses. Conclusions: In this large population of patients with idiopathic VTE we found that 3.4% of patients were diagnosed with cancer in the first year following VTE, and that older patients had a higher risk of cancer compared to younger patients. This risk is less than previously reported estimates of 4–12%. The value of occult cancer screening in patients with idiopathic VTE is uncertain. Disclosures: Tagalakis: Sanofi Aventis: Research Funding; Pfizer: Research Funding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".