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Record W2095717568 · doi:10.1586/14737167.4.2.135

Economic benefits of pioglitazone for treating patients with Type 2 diabetes

2004· article· en· W2095717568 on OpenAlexafffund
Loren D. Grossman, Christopher J. Longo

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2004
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsEli Lilly (Canada)University of Toronto
FundersEli Lilly CanadaEli Lilly and Company
KeywordsPioglitazoneMetforminType 2 diabetesMedicineSulfonylureaDiabetes mellitusInsulin resistanceIntensive care medicineInsulinInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Diabetes remains a significant economic burden to national healthcare systems. The traditional oral agents used to treat Type 2 diabetes do not address the underlying insulin resistance responsible for the development of diabetes. Newer medications, such as the thiazolidinediones, have been shown to reverse some of the metabolic processes believed to be responsible for the development of insulin resistance and ultimately, Type 2 diabetes. A comprehensive economic evaluation of pioglitazone using a modelling approach indicates that pioglitazone is a cost-effective therapy for patients with Type 2 diabetes when used in combination with either a sulfonylurea or metformin. This drug profile analyzes the clinical data on the use of piogltiazone for the treatment of Type 2 diabetes and the various economic evaluations of pioglitazone in the literature.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.032
GPT teacher head0.452
Teacher spread0.420 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2004
Admission routes2
Has abstractyes

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