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Record W2076968917 · doi:10.2174/1570161043476410

Regulation of Blood Flow by Prostaglandins

2004· review· en· W2076968917 on OpenAlex
Robert Boushel, Henning Langberg, Niels Risum, Michael Kjær

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

VenueCurrent Vascular Pharmacology · 2004
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsConcordia University
Fundersnot available
KeywordsHyperaemiaThromboxanesBlood flowVasodilationMedicineCyclooxygenaseInflammationEndocrinologyProstaglandinInternal medicineConnective tissueThromboxanePlateletChemistryBiochemistryPathologyEnzyme

Abstract

fetched live from OpenAlex

Prostaglandins (PGs) belong to the family of prostanoids together with thromboxanes and are produced mainly from arachadonic acid by the enzyme cyclooxygenase. PGs are known to stimulate platelet aggregation, mediate inflammation and edema, play a role in bone metabolism and in biological adaptation of connective tissues e.g. tendon. This review covers the role of PG for mediating tissue blood flow at rest and during increases in metabolic demand such as exercise and reactive hyperaemia. There is strong evidence that PGs contribute to elevate blood flow at rest and during reactive hyperaemia in a variety of tissues. Their role for regulating the large increases in muscle blood flow during exercise is less clear which may be explained by redundant mechanisms. Several interactions are known to exist between specific vasodilator substances, and therefore PGs can act in synergy with other substances and contribute to functional hyperaemia. Furthermore, there is evidence for differential, tissue-specific influences of PGs where their influence on blood flow during exercise may be profound.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.361
Teacher spread0.329 · 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