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Record W2752583015 · doi:10.14740/jocmr3144w

The Low-Dose (7.5 mg/day) Pioglitazone Therapy

2017· review· en· W2752583015 on OpenAlexvenueno aff
Hidekatsu Yanai, Hiroki Adachi

Bibliographic record

VenueJournal of Clinical Medicine Research · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPeroxisome Proliferator-Activated Receptors
Canadian institutionsnot available
Fundersnot available
KeywordsPioglitazoneMedicinePharmacologyUrologyInternal medicineEndocrinologyDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0320.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.527
GPT teacher head0.636
Teacher spread0.109 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations21
Published2017
Admission routes1
Has abstractyes

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