Endocrinology: What You May Have Missed in 2024
Bibliographic record
Abstract
During 2024, there were many practice-changing innovations in the field of endocrinology, particularly related to the use of glucagon-like peptide-1 receptor agonists (GLP-1RAs). From the substantial new evidence published in 2024, 10 studies are highlighted that offer critical information for clinicians who manage or comanage patients with endocrine disorders including prediabetes, diabetes, obesity, and hyperparathyroidism. Two of the 10 articles are focused on use of GLP-1RAs in multiple clinical settings not studied in the original GLP-1RA trials, including after bariatric surgery and before endoscopy. Two additional studies focused on GLP-1RA explore the risk for thyroid cancer in patients prescribed GLP-1RA and the effect of a GLP-1RA on chronic kidney disease in patients with type 2 diabetes. Three articles investigate opportunities for deintensification of insulin frequency or an alternate method of insulin delivery in patients with type 2 diabetes. One article explores the cardiometabolic effects of intermittent fasting in persons with prediabetes and type 2 diabetes. The last 2 articles explore the incidence of diabetes after SARS-CoV-2 infection and the skeletal effects of parathyroidectomy as a treatment of hyperparathyroidism. The results of each study have a direct effect on the delivery of care for patients with prediabetes, type 2 diabetes, and hyperparathyroidism.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.026 |
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".