Novel biomarkers to detect occult cancer in patients with unprovoked venous thromboembolism: Rationale and design of the PLATO-VTE study
Bibliographic record
Abstract
Occult cancer is detected in about 5% of patients with unprovoked venous thromboembolism (VTE) in the 12 months following VTE diagnosis. Current guidance suggests conducting a ‘limited’ cancer screening in these patients, consisting of medical history taking, physical examination, routine blood tests, chest X-ray, and age- and gender-specific testing, over full-body imaging. However, almost half of underlying cancers remain undetected with this approach. Blood-based liquid biopsies may provide an attractive addition or alternative to current cancer screening strategies, with a potentially higher detection rate while avoiding radiation or invasive testing. The PLATO-VTE study is an ongoing, investigator-initiated, multinational, prospective, observational cohort study comparing the sensitivity of novel biomarkers for detecting cancer with that of limited cancer screening in the setting of unprovoked VTE. Patients older than 40 years with a first episode of unprovoked VTE are eligible, while those with major and minor transient provoking risk factors for VTE are excluded. Patients undergo standard-of-care ‘limited’ cancer screening and are followed for 12 months for the occurrence of cancer. A blood sample for biomarker analysis is drawn within 10 days; a second sample is taken at 3 months to assess test result consistency over time. Three biomarkers are assessed: platelet mRNA, circulating tumor DNA, and plasma proteomics analysis. The sensitivity and predictive value of the biomarkers at baseline will be compared with those of limited screening. The results from the PLATO-VTE study may lead to reconsider current approaches for cancer screening in patients with unprovoked VTE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".