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Record W4224132648 · doi:10.3389/fcvm.2022.886266

The Evolving Story of Multifactorial Chylomicronemia Syndrome

2022· review· en· W4224132648 on OpenAlexaff
Martine Paquette, Sophie Bernard

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsUniversité de MontréalMontreal Clinical Research Institute
Fundersnot available
KeywordsMedicineHypertriglyceridemiaMetabolic syndromeDiseaseAcute pancreatitisType 2 diabetesObesityPancreatitisBioinformaticsPopulationSteatohepatitisIntensive care medicineDiabetes mellitusInternal medicinePediatricsFatty liverEndocrinologyEnvironmental healthBiologyTriglyceride

Abstract

fetched live from OpenAlex

Multifactorial chylomicronemia syndrome (MCS or type V hyperlipoproteinemia) is the most frequent cause of severe hypertriglyceridemia and is associated with an increased risk of acute pancreatitis, cardiovascular disease, and non-alcoholic steatohepatitis. The estimated prevalence of MCS in the North American population is 1:600-1:250 and is increasing due to the increasing prevalence of obesity, metabolic syndrome, and type 2 diabetes. Differentiating between familial chylomicronemia syndrome and MCS is crucial due to their very different treatments. In recent years, several cohort studies have helped to differentiate these two conditions, and recent evidence suggests that MCS itself is a heterogeneous condition. This mini-review will summarize recent literature on MCS, with a specific focus on the genetic determinants of the metabolic risk and the latest developments concerning the pharmacological and non-pharmacological treatment options for these patients. Possible research directions in this field will also be discussed.

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)0.002
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
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 head0.025
GPT teacher head0.279
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

Citations26
Published2022
Admission routes1
Has abstractyes

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