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The Coronary Microcirculation and Angiogenesis

2007· book-chapter· en· W81946244 on OpenAlexaff
Pierre Voisine, Joanna J. Wykrzykowska, Munir Boodhwani, David G. Harrison, Roger J. Laham, Frank W. Sellke

Bibliographic record

VenueHumana Press eBooks · 2007
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEicosanoids and Hypertension Pharmacology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMicroangiographyMicrocirculationCoronary circulationCardiologyMedicineBlood flowAngiogenesisInternal medicineIn vivoVascular resistanceCoronary flow reserveHemodynamicsPathologyBiology

Abstract

fetched live from OpenAlex

Resistance circulation of the heart is important in regulating the delivery of blood and nutrients to the myocardium. There has been a longstanding interest in studying its properties; however, prior to the mid-1980s, technical limitations made it difficult to directly study coronary microvessels either in situ or in vitro. Traditionally, studies of the coronary microcirculation had been limited to indirect assessments using measurements of coronary flow and calculations of coronary resistance, which provided a great deal of insight into the properties of the intact coronary circulation. Significantly more has been learned in the last 15 yr as new in vivo and in vitro approaches have been developed for direct study of coronary microvessels ( 1 – 4 ). Furthermore, development of microangiography methods allowed for visualization of the coronary microcirculation in mammals ( 5 , 6 ). The most important finding of these studies was the inhomogeneity of the resistance vessels ( 7 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.000
metaresearch head score (Gemma)0.000
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: Other · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.005

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 head0.062
GPT teacher head0.284
Teacher spread0.222 · 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

Citations2
Published2007
Admission routes1
Has abstractyes

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