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Record W3010928772 · doi:10.1186/s40168-020-00821-0

Gut microbiota and cardiovascular disease: opportunities and challenges

2020· review· en· W3010928772 on OpenAlex

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

VenueMicrobiome · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsInterior HealthUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsGut floraBiologyMicrobiomeDiseaseGut microbiomeCoronary artery diseaseImmunologyImmune systemBioinformaticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Coronary artery disease (CAD) is the most common health problem worldwide and remains the leading cause of morbidity and mortality. Over the past decade, it has become clear that the inhabitants of our gut, the gut microbiota, play a vital role in human metabolism, immunity, and reactions to diseases, including CAD. Although correlations have been shown between CAD and the gut microbiota, demonstration of potential causal relationships is much more complex and challenging. In this review, we will discuss the potential direct and indirect causal roots between gut microbiota and CAD development via microbial metabolites and interaction with the immune system. Uncovering the causal relationship of gut microbiota and CAD development can lead to novel microbiome-based preventative and therapeutic interventions. However, an interdisciplinary approach is required to shed light on gut bacterial-mediated mechanisms (e.g., using advanced nanomedicine technologies and incorporation of demographic factors such as age, sex, and ethnicity) to enable efficacious and high-precision preventative and therapeutic strategies for CAD.

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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.107
GPT teacher head0.289
Teacher spread0.182 · 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