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Record W2099755736 · doi:10.3402/jev.v4.26901

Extracellular vesicles, tissue factor, cancer and thrombosis – discussion themes of the ISEV 2014 Educational Day

2015· article· en· W2099755736 on OpenAlexaff
Chris Gardiner, Paul Harrison, Mattias Belting, Anita N. Böing, Elena Campello, Bob S. Carter, Mary E.W. Collier, Frank A. W. Coumans, Camille Ettelaie, Nick van Es, Fred H. Hochberg, Nigel Mackman, Robert C. Rennert, J. Thaler, Janusz Rak, Rienk Nieuwland

Bibliographic record

VenueJournal of Extracellular Vesicles · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill UniversityMontreal Children's Hospital
FundersNational Center for Advancing Translational SciencesMedical Research Council
KeywordsExtracellular vesiclesThrombosisCancerExtracellularVenous thromboembolismMedicineVesicleTissue factorPathologyBioinformaticsCancer researchInternal medicineCoagulationBiologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

Although the association between cancer and venous thromboembolism (VTE) has long been known, the mechanisms are poorly understood. Circulating tissue factor-bearing extracellular vesicles have been proposed as a possible explanation for the increased risk of VTE observed in some types of cancer. The International Society for Extracellular Vesicles (ISEV) and International Society on Thrombosis and Haemostasis (ISTH) held a joint Educational Day in April 2014 to discuss the latest developments in this field. This review discusses the themes of that event and the ISEV 2014 meeting that followed.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.022
GPT teacher head0.291
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations83
Published2015
Admission routes1
Has abstractyes

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