Bibliographic record
Abstract
We have utilized epidemiological data to address three questions in patients with cancer and venous thromboembolism (VTE): (1) What is the risk for occult cancer in patients with idiopathic versus secondary VTE? (2) What is the risk for thrombosis in patients with cancer (vs. noncancer patients)? (3) What is the risk of recurrent VTE in cancer patients with an initial episode of VTE compared to noncancer patients? The risk for a new cancer diagnosis within 6-12 months of the diagnosis of idiopathic VTE (including pulmonary embolism) is well supported by retrospective analyses of large numbers of unselected patients, population-based retrospective cohort analyses from large registries and prospective studies. The odds ratios for these studies are in the range of 4- to 7-fold increased risk. In surgical patients with known cancer the odds ratio for an episode of postoperative VTE is approximately 2, when compared to a control group of noncancer patients subjected to the same procedures. A similar odds ratio of approximately 2 exists for the relative risk for recurrence of VTE in the first 3 months after an initial episode in cancer patients treated with heparin and warfarin (Coumadin) compared to noncancer patients. Therefore, patients with idiopathic VTE are at increased risk for occult cancer and cancer patients are at increased risk for VTE. Appropriate studies are underway to determine the best strategies for anticoagulant management of these patients.
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 | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".