Systematic Review: The Trousseau Syndrome Revisited: Should We Screen Extensively for Cancer in Patients with Venous Thromboembolism?
Bibliographic record
Abstract
BACKGROUND: Identifying previously undiagnosed cancer in patients with newly diagnosed venous thromboembolism (VTE) is important. Screening for malignant conditions can potentially diagnose more cases of cancer and at earlier stages, thereby preventing cancer-associated morbidity and perhaps mortality. PURPOSE: To summarize the period prevalence of previously undiagnosed cancer at baseline (within 1 month of VTE diagnosis), 6 months, and 12 months after VTE diagnosis and to quantify the additional value of an extensive cancer screening strategy (limited screening plus imaging techniques or tumor marker measurement) at baseline compared with more limited screening (history, physical examination, and simple widely available tests) at baseline. DATA SOURCES: MEDLINE, EMBASE, the Cochrane Register of Controlled Trials, and Evidence-Based Medicine Reviews. STUDY SELECTION: A total of 36 studies that reported the prevalence of undiagnosed cancer at baseline, 6 months, and 12 months were selected. Fourteen articles and 1 abstract also met inclusion criteria for the assessment of extensive versus limited cancer screening. DATA EXTRACTION: Two reviewers independently extracted data onto standardized forms. DATA SYNTHESIS: The period prevalence of previously undiagnosed cancer in patients with unprovoked VTE was 6.1% (95% CI, 5.0% to 7.1%) at baseline and 10.0% (CI, 8.6% to 11.3%) from baseline to 12 months. An extensive screening strategy using computed tomography of the abdomen and pelvis statistically significantly increased the proportion of previously undiagnosed cancer detected from 49.4% (CI, 40.2% to 58.5%) (with limited screening alone) to 69.7% (CI, 61.1% to 77.8%) in patients with unprovoked VTE. LIMITATION: The investigators could not determine complication rates, cost-effectiveness, and difference in morbidity and mortality associated with extensive screening strategies. CONCLUSION: Previously undiagnosed cancer is frequent in patients with unprovoked VTE. Many cases of previously undiagnosed cancer are missed by screening. An extensive cancer screening strategy detects more malignant conditions than does a limited screening strategy.
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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 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".