Bibliographic record
Abstract
Pioglitazone is one of thiazolidinedione derivatives, which stimulates nuclear peroxisome proliferator-activated receptor gamma and improves glucose and lipid metabolism and insulin sensitivity. A recent systematic review and meta-analysis showed that pioglitazone therapy was associated with a lower risk of major adverse cardiovascular events in patients with pre-diabetes and diabetes. Further, in a cohort study of patients with type 2 diabetes, pioglitazone therapy was associated with a statistically significant decrease in the risk of all-cause mortality. Despite these beneficial effects, the meta-analysis showed that pioglitazone therapy had higher risks of heart failure, bone fractures, edema and weight gain. To find out the efficacy and safety of the low-dose (7.5 mg/day) pioglitazone therapy, we reviewed the dose-response of pioglitazone on favorable effects and adverse effects due to pioglitazone, by searching the reports on effects of daily dose of 7.5 mg and/or 15 mg and/or 30 mg of pioglitazone. The low-dose pioglitazone therapy may show the same degree of improvements in glucose and lipid metabolism, fatty liver, insulin resistance, and adiponectin as the standard- and high-dose pioglitazone therapy. Furthermore, the low-dose pioglitazone therapy may also show less adverse effects on weight gain, edema and heart failure as compared with the standard- and high-dose pioglitazone therapy.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".