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Record W2754310746 · doi:10.1124/pr.115.012062

Connexins in Cardiovascular and Neurovascular Health and Disease: Pharmacological Implications

2017· review· en· W2754310746 on OpenAlexafffund
Luc Leybaert, Paul D. Lampe, Stefan Dhein, Brenda R. Kwak, Péter Ferdinandy, Eric C. Beyer, Dale W. Laird, Christian C. Naus, Colin Green, Rainer Schulz

Bibliographic record

VenuePharmacological Reviews · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnexins and lens biology
Canadian institutionsUniversity of British ColumbiaWestern University
FundersNational Eye InstituteNational Science FoundationCanadian Institutes of Health ResearchVlaamse regeringHungarian Scientific Research FundEuropean Foundation for the Study of DiabetesNational Research, Development and Innovation OfficeUniversiteit GentSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institutes of HealthNational Institute of General Medical SciencesEuropean Association for the Study of Diabetes
KeywordsGap junctionConnexinConnexonCell biologyPannexinNeuroscienceBiologyMyocyteCell typeIntracellularCellBiochemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.196
GPT teacher head0.450
Teacher spread0.254 · 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

Citations247
Published2017
Admission routes2
Has abstractno

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