Performance of 18F-fluorodesoxyglucose positron-emission tomography/computed tomography for cancer screening in patients with unprovoked venous thromboembolism: Results from an individual patient data meta-analysis
Bibliographic record
Abstract
INTRODUCTION: F-Fluorodesoxyglucose Positron-Emission Tomography/Computed Tomography (FDG PET/CT) for occult cancer screening in patients with unprovoked VTE. METHODS: This was a pre-specified analysis of a systematic review and individual patient data meta-analysis including prospective studies assessing cancer screening in patients with unprovoked VTE. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FDG PET/CT were calculated based on cancer diagnosis during a 1-year follow-up period. RESULTS: Four studies were identified as using FDG PET/CT as part of their extensive screening strategy. Out of the 332 patients who underwent FDG PET/CT, the scan was interpreted as positive in 67 (20.2%), as equivocal in 27 (8.1%), and as negative in 238 (71.7%). Seventeen (5.1%) patients were diagnosed with cancer at inclusion or during the 12-month follow up period. All cancers were diagnosed at initial screening. Pooled sensitivity, specificity, NPV, and PPV were 87.3% (95% CI, 55.3 to 97.4), 70.2% (95% CI, 48.2 to 85.6), 98.9% (95% CI, 94.3 to 99.7), and 17.9% (95% CI, 8.5 to 33.6), respectively. CONCLUSION: FDG PET/CT appears to have satisfactory accuracy indices for cancer diagnosis in patients with unprovoked VTE. In particular, it exhibits a very high negative predictive value and could be used to rule out the presence of an underlying occult malignancy in this setting.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".