MétaCan
Menu
Back to cohort
Record W4291513501 · doi:10.1080/14656566.2022.2112945

Colesevelam – a bile acid sequestrant for treating hypercholesterolemia and improving hyperglycemia

2022· article· en· W4291513501 on OpenAlexaff
Oluwayemisi Esan, Adie Viljoen, Anthony S. Wierzbicki

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2022
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineBile acidAdverse effectInternal medicineCholesterolDiabetes mellitusGlycemicPharmacologyEndocrinologyGastroenterology

Abstract

fetched live from OpenAlex

Introduction Low density Lipoprotein cholesterol)LDL-C) levels show a clear relationship with cardiovascular disease (CVD). Statins are first line agents to reduce LDL-C and CVD risk. However, combination lipid-lowering therapy is often required to achieve large reductions in LDL-C.Area covered Colesevelam HCl is a bile acid sequestrant (BAS), which reduces LDL-C by 16–22% in monotherapy and adds a further 12–14% reduction in LDL-C when combined with other lipid-lowering drugs. Like statins, colesevelam reduces C-reactive protein levels by 16% in monotherapy and additional 6% when added to statins. Colesevelam also reduced HbA1c by 4 mmol/mol (0.5%) when used alone and added to other hypoglycemic drugs in studies of patients with diabetes .Expert Opinion Bile acid sequestrants reduce LDL-C and HbA1c and have some CVD outcome evidence. The uses of these agents are limited in patients with gastrointestinal disease or high triglycerides due to adverse effects on gut function and raising triglycerides and they interfere with the absorption of lipid-soluble drugs. Colesevelam has a higher bile acid binding capacity, and fewer adverse effects than other BAS. Colesevelam may be useful as a third line agent for treatment of hypercholesterolemia with some additional specific benefits on glycemic control.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.032
GPT teacher head0.345
Teacher spread0.313 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on PharmacotherapySame topicLipoproteins and Cardiovascular HealthFrench-language works237,207