Predictors of outcomes in patients with type 2 diabetes in the lixisenatide <scp>GetGoal</scp> clinical trials
Bibliographic record
Abstract
AIMS: To explore the treatment outcomes in adult patients with type 2 diabetes (T2D) enrolled in the GetGoal trials of lixisenatide (LIXI), and the predictive effects of baseline characteristics on outcomes. METHODS: This study was a pooled analysis of patient-level data from the LIXI GetGoal studies comparing LIXI and placebo. Patients were divided into baseline therapy groups: those receiving oral antidiabetes drugs (OADs) at baseline (n = 2760) or those receiving basal insulin at baseline (n = 1198). RESULTS: Compared with placebo, LIXI treatment led to significantly greater reductions in glycated haemoglobin (HbA1c), and greater achievement of the composite endpoint of HbA1c <7.0% (53 mmol/mol) with no symptomatic hypoglycaemia and no weight gain in either the OAD (34% vs 18%; P < .0001) or the basal insulin groups (19% vs 10%; P < .0001). Treatment with LIXI was associated with a greater percentage of patients experiencing a symptomatic hypoglycaemic event compared with placebo in both the OAD (5% vs 3%; P = .0098) and basal insulin groups (27% vs 17%; P < .0001). In assessing baseline factors that were predictors of treatment outcomes, only baseline HbA1c and LIXI treatment were strong predictors of outcomes in both the OAD and basal insulin groups. No other baseline characteristic had such a large or consistent clinically relevant predictive effect across treatment outcomes. CONCLUSIONS: The results from this study show that irrespective of baseline characteristics, LIXI treatment, as an add-on to OAD or basal insulin therapy, is effective in reducing HbA1c and achieving composite endpoints.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".