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Thrombosis and Cancer: The Role of Screening for Occult Cancer and Recognizing the Underlying Biological Mechanisms

2006· review· en· W2106910258 on OpenAlex
Agnes Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHematology · 2006
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMalignancyMedicineCancerThrombosisVenous thrombosisOccultDiseaseVenous thromboembolismOncologyInternal medicineBioinformaticsPathologyBiology

Abstract

fetched live from OpenAlex

The association between cancer and thrombosis is well recognized. What is not known, however, is the exact relationship between these two common medical conditions. Although the development of venous thromboembolism (VTE) in a patient with known cancer is the most common presentation, in some patients, VTE may precede the diagnosis of malignancy by many months. The variation in clinical presentation is likely due to the heterogeneous biology of different tumor types and also reflects the limitations of detection or available diagnostic methods. Accumulating evidence now suggests that critical oncogenic events may also trigger activation of the coagulation cascade, leading to a prothrombotic environment that not only manifests as venous thromboembolic disease but also promotes the growth and progression of the malignancy. This chapter will review the evidence for screening for occult malignancy in patients presenting with unprovoked or idiopathic thrombosis, briefly outline the known biological relationships between malignancy and thrombosis, and summarize the clinical data on the potential anticancer effects of low molecular weight heparins (LMWHs).

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.199
GPT teacher head0.412
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it