Should we screen extensively for cancer after unprovoked venous thrombosis?
Bibliographic record
Abstract
#### What you need to know How far to go in screening patients with an unprovoked venous thromboembolism (VTE) for an occult cancer is a clinical dilemma. Unprovoked VTE, either deep vein thrombosis or pulmonary embolism, can be the first manifestation of an undiagnosed cancer. Until recently, the literature suggested that up to 10% of such patients would be diagnosed with a cancer in the year after their diagnosis of VTE.1 However, the incidence of occult cancer in patients studied in two recent, high quality, randomised controlled trials was only about 4%.23 This drop in the proportion of people with occult cancer may require an adjustment in the clinical approach. Fig 1⇓ outlines a conservative approach and a more detailed approach to investigating such patients. Extensive screening has become the standard of care, though it is based on limited data. Fig 1 Occult cancer screening strategies in unprovoked venous thromboembolism (from NICE guidelines 20124) However, high quality data from recently completed trials discussed below suggest that extensive screening strategies may not provide additional value over routine cancer screening in the frequency of cancer detection in these patients. #### Search strategy and study selection We searched PubMed (from inception to 31 December 2016) for randomised controlled trials and systematic reviews using the search terms “cancer screening,” “venous thromboembolism,” “unprovoked,” “meta-analysis,” and “randomized controlled trial.” We reviewed articles published in English between 2012 (publication of NICE guidelines) and 2016. We also searched the Cochrane Library and …
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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 | 0.016 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".