Goal achievement of <scp>HbA1c</scp> and <scp>LDL‐</scp> cholesterol in a randomized trial comparing colesevelam with ezetimibe: <scp>GOAL‐RCT</scp>
Bibliographic record
Abstract
Abstract Aim To compare the efficacy and safety of colesevelam and ezetimibe as second‐line low density lipoprotein‐cholesterol (LDL‐c)‐lowering options in type 2 diabetes (T2D). Materials and Methods GOAL‐RCT is a 24‐week, open‐label, randomized, pragmatic clinical trial. Subjects with T2D with uncontrolled HbA1c (7.1%‐10%) and LDL‐c (>2.0 mmol/L) were randomized 1:1 to colesevelam 3.75 g or ezetimibe 10 mg daily. The primary composite outcome was the proportion of participants achieving an LDL‐c target of ≤2.0 mmol/L and HbA1c target of ≤7.0%. Intention to treat analysis was performed. Results Two hundred subjects were enrolled: mean age 59 ± 10 years; mean HbA1c 8.0%; mean LDL‐c 2.5 mmol/L; 97% on statin therapy. The primary composite outcome was achieved by similar proportions of participants with colesevelam (14.6%) and ezetimibe (10.5%) ( P non‐inferiority < .001, P superiority = .41). LDL‐c reduction from baseline was less with colesevelam compared with ezetimibe (14.0% vs. 23.2%, P < .01), as was the proportion of subjects achieving an LDL‐c target of ≤2.0 mmol/L (47.6% and 67.0%, respectively; P = .007). Mean HbA1c was reduced with colesevelam (−0.26 ± 0.10%), while no change was observed with ezetimibe (difference P = .06). Adverse events and discontinuation rates were higher for colesevelam (20.2% and 31.1%) compared with ezetimibe (7.2% and 6.2%), respectively. Conclusions Among subjects with T2D, the initiation of colesevelam or ezetimibe led to similar achievement of primary composite outcome (LDL‐c and HbA1c within target), with ezetimibe recording a greater LDL‐c reduction and better tolerability than colesevelam.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".