Trousseau’s Syndrome Revisited: Should We Screen Extensively for Malignancy in Patients with Venous Thromboembolism? a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background: Identifying previously undiagnosed malignancy in patients with newly diagnosed venous thromboembolism (VTE) is important. Malignancy screening can potentially diagnose more cancers and at earlier stage, thereby preventing malignancy associated morbidity and perhaps mortality. Purpose: To summarize the period prevalence of previously undiagnosed malignancy at baseline (within 1 month of VTE diagnosis), 6 and 12 months following VTE. To quantify the additional value of an “extensive” malignancy screening strategy at baseline compared to a more “limited” screen (history, physical exam and simple widely available tests) at baseline. Data Source: A systematic literature search strategy was conducted using MEDLINE, EMBASE, the Cochrane Register of Controlled Trials and all EBM Reviews. Study Selection: We selected 36 studies that reported the prevalence of undiagnosed malignancies at baseline, at 6 and 12 months. Fourteen articles and one abstract also met inclusion criteria for the assessment of “extensive” versus “limited” malignancy screening. Data extraction: Two reviewers independently extracted data onto standardized forms. Data Synthesis: The period prevalence of previously undiagnosed malignancy in patients with unprovoked VTE is 6.1% (95% confidence intervals (CI): 5.0 to 7.1) at baseline and 10.0% (95% CI: 8.6 to 11.3) from baseline to 12 months. An “extensive” malignancy screening strategy using computed tomography of the abdomen/pelvis significantly increases the proportion of previously undiagnosed malignancy detected from 49.4% (95% CI: 40.2 to 58.5) (limited screening alone) to 69.7% (95% CI: 61.1 to 77.8) in patients with unprovoked VTE. Limitations: Unable to determine complication rates, cost-effectiveness and difference in morbidity and mortality associated with “extensive” screening strategies. Conclusion: Previously undiagnosed malignancies are frequent in patients with unprovoked VTE. Malignancy screening using an “extensive” screening strategy detects more malignancies compared to a limited screening strategy. Computed tomography of the abdomen/pelvis should be considered in the diagnostic work up of previously undiagnosed malignancy in patients with unprovoked VTE.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".