Colesevelam – a bile acid sequestrant for treating hypercholesterolemia and improving hyperglycemia
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".