Diabetes clinical trials: helped or hindered by the current shift in regulatory requirements?
Bibliographic record
Abstract
Glycaemic control is an inadequate surrogate marker of cardiovascular event reduction in patients with type 2 diabetes. Clinical trials to date have been unsuccessful in identifying a therapeutic approach that addresses the underlying problem in diabetes (glycaemic control) and reduces cardiovascular risk. The potential for some agents to increase the risk of cardiovascular events has led to substantial changes in regulatory requirements for new anti-diabetic therapies. These requirements, while key to ensuring the cardiovascular safety of new agents, fail to emphasize the need to show clinical benefits, such as less visual impairment, less need for dialysis, or fewer cardiovascular events and deaths. Changes in test results such as glycaemic control, serum creatinine, micro-albuminuria, or retinopathy are inadequate surrogates. Regulators should consider the potential advantages of offering extended patent protection in order to encourage companies to conduct long-term trials in diabetes and many other chronic medical conditions. Cooperative efforts among physicians, clinical trialists, regulators, and sponsors are needed to address unresolved issues including re-defining therapeutic targets that are meaningful to patients with diabetes, determining the appropriate length of follow-up for future trials, and considering the ethical and operational challenges of non-inferiority designs.
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 | 0.129 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".