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Record W2063051980 · doi:10.1097/moh.0b013e328345a3d1

Endothelial activation, dysfunction and permeability during severe infections

2011· review· en· W2063051980 on OpenAlexafffund
Warren L. Lee, W. Conrad Liles

Bibliographic record

VenueCurrent Opinion in Hematology · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto General HospitalSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsSepsisEndothelial activationEndothelial dysfunctionAngiopoietin 2EndotheliumVascular permeabilityInflammationAngiopoietinSeptic shockPathogenesisAngiopoietin receptorMedicineImmunologyEdemaEndothelial stem cellShock (circulatory)Barrier functionAngiogenesisBiologyPathologyInternal medicineVascular endothelial growth factorCell biology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Over the last few years, there have been major advances in our understanding of the role of the microvascular endothelium in the pathogenesis of severe, systemic infections. RECENT FINDINGS: Endothelial activation and dysfunction contribute directly to the morbidity and mortality of sepsis and other, severe systemic infections. The end-result of diffuse endothelial activation and dysfunction may be the loss of microvascular barrier integrity, leading to tissue edema, shock and multiple organ failure. Endothelial activation also leads to an increase in angiopoietin-2, which is known to destabilize barrier function and promote inflammation. SUMMARY: The ratio of the secreted endothelial growth factors, angiopoietin-2 and angiopoietin-1 appears to be a useful prognostic tool during severe infections. Finally, agents that enhance endothelial barrier integrity may prove useful as therapies for sepsis.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.068
GPT teacher head0.355
Teacher spread0.287 · 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
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

Citations114
Published2011
Admission routes2
Has abstractyes

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