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Record W4314446201 · doi:10.56367/oag-037-10518

The haplogroup gap: The ticking time bomb of cardiometabolic disease in developing nations

2023· article· en· W4314446201 on OpenAlexaff
Kimberly J. Dunham‐Snary

Bibliographic record

VenueOpen Access Government · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsQueen's University
Fundersnot available
KeywordsDiseaseMedicineObesityType 2 diabetesDiabetes mellitusAtherosclerotic cardiovascular diseaseEnvironmental healthInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

The haplogroup gap: The ticking time bomb of cardiometabolic disease in developing nations Cardiometabolic disease (CMD) greatly increases an individual's risk for developing cardiovascular disease (CVD) and/or Type II diabetes (2), with the former being a leading cause of death worldwide. Decades of research efforts have significantly improved our understanding of cardiometabolic disease as a multifactorial ‘whole-body' pathology caused not only by common ‘modifiable' risk factors (such as exercise and dietary choices), but also increased inflammation within our muscles and fat, as well as inherited genetic risk. The genetic aspect of cardiometabolic disease has proven vexing to the medical and research communities – hundreds of genes have been associated with the hallmarks of CMD, yet none occur at a frequency that would explain the explosion in obesity and CMD rates documented worldwide over the past 25 years.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.043
GPT teacher head0.358
Teacher spread0.315 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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