Bibliographic record
Abstract
The two major metabolic perturbations resulting in hyperglycaemia in type 2 diabetes are insulin resistance and insulin deficiency. Insulin resistance occurs in peripheral organs (muscle and fat), leading to decreased glucose uptake and utilisation, and in liver, leading to increased hepatic glucose production. Thiazolidinediones, pharmacological ligands for PPAR gamma, can modulate the expression of genes influencing carbohydrate and lipid metabolism. Pioglitazone, a recently introduced thiazolidinedione, improves glycaemic control and lipid profiles in people with type 2 diabetes. Some of the possible mechanisms of improving glycaemic control include (a) increase in GLUT-1 and GLUT-4, (b) enhancement of insulin signalling, (c) decrease in tumour necrosis factor-alpha action, (d) reduction in plasma free fatty acid and (e) decrease in PEPCK. Together these can increase glucose uptake and utilisation in the peripheral organs and decrease gluconeogenesis in the liver. Possible mechanisms resulting in more desirable lipid profiles include an increase in phosphodiesterase-3B resulting in reduced intra-cellular lipolysis in adipocytes and an increase in lipoprotein lipase resulting in enhanced clearance of triglyceride-rich lipoproteins(TRLs). Pioglitazone, used as monotherapy or in combination with sulphonylurea, biguanide or insulin, improves glycaemic control, lowers serum triglycerides and raises high density lipoprotein (HDL)-cholesterol. It enhances hepatic and peripheral insulin sensitivity. In clinical trials, there has been no evidence of hepatotoxicity or increased incidence of elevated serum ALT in subjects taking pioglitazone compared with placebo.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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".