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Record W2417939480 · doi:10.1055/s-2004-822967

Contribution of the Hemostatic System to Angiogenesis in Cancer

2004· review· en· W2417939480 on OpenAlexaff
Marek Z. Wojtukiewicz, Ewa Sierko, Janusz Rak

Bibliographic record

VenueSeminars in Thrombosis and Hemostasis · 2004
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsAngiogenesisFibrinFibrinolysisCancer researchThrombinHemostasisCoagulationCancerFibrinogenImmunologyMedicineBiologyPlateletCell biologyInternal medicine

Abstract

fetched live from OpenAlex

It is now recognized that the hemostatic system plays an important role in cancer growth and dissemination, processes known to be vitally dependent on new tumor blood vessel formation (angiogenesis). There is also an increasing body of evidence supporting the link between the various components of the coagulation/fibrinolysis systems and angiogenic activity in cancer patients. Tissue factor (TF), thrombin, fibrinogen, fibrin, and plasminogen activation system, as well as platelets, all are able to promote angiogenesis. On the other hand, coagulation inhibitors, as well as cryptic (proteolytically released) domains of hemostatic proteins, are also known to act as angiogenesis inhibitors. Indeed, modulation (stimulation or inhibition) of angiogenesis may result from either classical functions of various molecular components of the hemostatic cascade, their less studied "alternative" activities, or both. Although much remains to be understood about this complex circuitry these considerations support the judicious use of anticoagulants in patients with malignancy as well as encourage the search for novel antiangiogenic activities that may reside within molecular and cellular components of the hemostatic system.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.365
Teacher spread0.318 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations67
Published2004
Admission routes1
Has abstractyes

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