Bibliographic record
Abstract
Horizon Scan reports provide brief summaries of information regarding new and emerging health technologies; Heath Technology Update articles typically focus on a single device or intervention. These technologies are identified through the CADTH Horizon Scanning Service as topics of potential interest to health care decision-makers in Canada. This Horizon Scan summarizes the available information regarding an emerging technology, tirzepatide, for the treatment of hyperglycemia in adults with type 2 diabetes. Type 2 diabetes (T2D) is a metabolic disease where blood glucose concentrations cannot be maintained at a normal level (hyperglycemia). Several antihyperglycemia drugs are currently available for the treatment of T2D including metformin, insulin secretagogues (meglitinides, sulfonylureas), dipeptidyl peptidase-4 (DPP4) inhibitors, sodium glucose cotransporter 2 (SGLT2) inhibitors, glucagon-like polypeptide-1 (GLP-1) receptor agonists, thiazolidinediones, alpha-glucosidase inhibitors, and slow/fast-acting insulin analogues. Despite the number of drugs currently available to mitigate hyperglycemia, a significant unmet need for new therapeutics still exists. Tirzepatide (LY3298176; Eli Lilly Inc.) is a first in class dual glucagon-like polypeptide-1 (GLP-1) receptor and glucose-dependent insulinotropic polypeptide (GIP) receptor agonist currently under development to treat hyperglycemia and obesity in individuals with T2D. To date, phase III clinical trials have been completed and several other trials are in progress to determine the efficacy of tirzepatide to reduce hyperglycemia (SURPASS studies) and obesity (SURMOUNT-2 study) in adults with T2D. This Horizon Scan bulletin focuses on the glycemic effect of tirzepatide in T2D. In summary, tirzepatide demonstrates efficacy in reducing mean glycated hemoglobin (A1C) compared to placebo, semaglutide, insulin degludec, and insulin glargine. Studies investigating the possible effect of tirzepatide on cardiovascular outcomes are currently ongoing.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".