Screening for cancer in patients with unprovoked venous thromboembolism: protocol for a systematic review and individual patient data meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Occult cancer is present in 4%-9% of patients with unprovoked venous thromboembolism (VTE). Screening for cancer may be considered in these patients, with the aim to diagnose cancers in an early, potentially curable stage. Information is needed about the risk of occult cancer, overall and in specific subgroups, additional risk factors and on the performance of different screening strategies. METHODS AND ANALYSIS: MEDLINE, Embase and CENTRAL databases were searched from November 2007 to January 2016 for prospective studies that had evaluated protocol-mandated screening for cancer in patients with unprovoked VTE and with at least 12 months' follow-up. Two reviewers independently assessed articles for eligibility. Ten eligible studies were identified and individual patient data were obtained from each of them. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool . Generalised linear mixed-effects models was used to calculate estimates in a one-stage meta-analytic approach, overall and in a number of subgroups, including patients undergoing limited screening only, elderly patients, patients with previous VTE, smokers and patients using oestrogens. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review and individual patient data meta-analysis. Findings have been submitted for publication in peer-reviewed journals and presentations at national and international conferences to provide clinicians and other decision-makers with valid and precise risk estimates of occult cancer, overall and in specific clinical subgroups, with risk factors for occult cancer, with estimates of the diagnostic performance of limited screening and with an exploration of the benefit of extensive screening strategies.
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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 | 0.076 | 0.122 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.025 | 0.032 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".