Occult cancer detection in venous thromboembolism: the past, the present, and the future
Bibliographic record
Abstract
Unlabelled Box •Unprovoked venous thromboembolism (VTE) may be the first manifestation of an undiagnosed cancer. •The rate of occult cancer detection in patients with unprovoked VTE is approximately 5%. •Clinicians should keep a low threshold of suspicion for occult cancer in these patients. •Patients should only undergo a limited as well as age‐ and gender‐specific cancer screening. Unprovoked venous thromboembolism (VTE) can be the first manifestation of an undiagnosed cancer. Recently published studies have suggested that approximately 4‐5% of patients with new unprovoked VTE will be diagnosed with cancer within 12 months of follow‐up. Therefore, it is important for clinicians to keep a low threshold of suspicion for occult cancer in this patient population. After an unprovoked VTE diagnosis, patients should undergo a thorough medical history, physical examination, basic laboratory investigations (ie, complete blood count and liver function tests), chest X‐ray, as well as age‐ and gender‐specific cancer screening (breast, cervical, colon, and prostate). More intensive cancer screening including additional investigations (eg, computed tomography of the abdomen/pelvis) does not seem to increase the rate of occult cancer detection, decrease cancer‐related morbidity, or increase survival or cost‐effectiveness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".