RETRACTED: A shared comparison of diabetes mellitus and neurodegenerative disorders
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Diabetes mellitus (DM) is one of the most common diseases in the world population, associated with obesity, pancreatic endocrine changes, cardiovascular disease, renal glomerular disease, cerebrovascular disease, peripheral neuropathy, neurodegenerative disease, retinal disease, sleep apnea, some of which are bundled into the metabolic syndrome. The main characteristic of this disease is hyperglycemia, and often with albuminuria. Nevertheless, the classic features, with ketoacidosis in the extreme, are only a first layer of description of this condition. The description of the islet cells of the endocrine pancreas was first described by Opie, and the discovery of insulin by tying off the exocrine pancreatic ducts followed. We later find that the β-cells secrete insulin and glucagon, which synchronously stimulate or suppress glycogenolysis, and that insulin is essential for glucose intake into the cell. There are yet two other layers for our understanding of diabetes and the effects of its dysfunction, which is the basis for understanding the system-wide expression of the disease. We describe the molecular basis for the central nervous system neuropathic diseases that are associated with both Type 1 DM (T1DM) and Type 2 DM (T2DM), but more so with T2DM. T2DM is an autoimmune disease that destroys the insulin secreting islet cells. T2DM is the diabetes that is associated with an imbalance in the glucagon/insulin homeostasis that leads to the formation of amyloid deposits in the brain, pancreatic islet cells, and possibly the kidney glomerulus.
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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".