Incidental venous thromboembolism: is anticoagulation indicated?
Bibliographic record
Abstract
Patients with cancer have a high risk of venous thromboembolism (VTE) and about one-half of these events are incidentally detected. The prognosis of incidental VTE appears to be similar to symptomatic events, with comparably high rates of recurrent VTE in this patient population. In the absence of major contraindications, anticoagulant treatment with low-molecular-weight heparin for 3 to 6 months is generally recommended for incidental proximal deep vein thrombosis as well as for incidental pulmonary embolism that involves multiple subsegmental or more proximal pulmonary arteries. The decision of whether to extend treatment beyond 3 to 6 months should be evaluated on a case-by-case basis after periodic reassessment of the risks factors for bleeding and recurrent VTE while also taking into account patient preferences. The clinical relevance of a single incidental subsegmental pulmonary embolism without concomitant deep vein thrombosis is uncertain and either a watchful approach or a shorter course of anticoagulation to minimize the bleeding risk may also be considered. Preliminary evidence suggests that anticoagulation treatment may be beneficial for cancer patients with incidental distal deep vein thrombosis or incidental splanchnic vein thrombosis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".