Incretin-Based Drugs and Adverse Pancreatic Events: Almost a Decade Later and Uncertainty Remains
Bibliographic record
Abstract
Over the past few years, substantial clinical data have been presented showing that incretin-based therapies are effective glucose-lowering agents. Specifically, glucagon-like peptide 1 receptor agonists demonstrate an efficacy comparable to insulin treatment with minimal hypoglycemia and have favorable effects on body weight. Thus, many of the unmet clinical needs noted from prior therapies are addressed by these agents. However, even after many years of use, many continue to raise concerns about the long-term safety of these agents and, in particular, the concern with pancreatitis. This clearly remains a complicated topic. Thus, in this issue of Diabetes Care, we continue to update our readers on this very important issue by presenting two studies evaluating incretin-based medications and risk of pancreatitis. Both have undergone significant revisions based on peer review that provided significant clarification of the data. We applaud both author groups for being extremely responsive in providing the additional data and revisions requested by the editorial team. As such, because of the critical peer review, we feel both articles achieve the high level we require for Diabetes Care and are pleased to now present them to our readers. In keeping with our aim to comprehensively evaluate this topic, we asked for additional commentaries to be prepared. In the narrative outlined below, Dr. Laurent Azoulay provides a commentary about the remaining uncertainty in this area and also discusses the results from a nationwide population-based case-control study. In the narrative preceding Dr. Azoulay's contribution, Prof. Edwin A.M. Gale provides a commentary on the report that focuses on clinical trials of liraglutide in the treatment of diabetes. From the journal's perspective, both of the articles on pancreatitis and incretin-based therapies reported in this issue have been well vetted, and we feel both of the commentaries are insightful.
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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 | 0.051 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.027 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".