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Record W2899412681 · doi:10.1111/dme.13854

A systematic review of observational studies of the association between pioglitazone use and bladder cancer

2018· review· en· W2899412681 on OpenAlexaff
Enrico Ripamonti, Laurent Azoulay, Michał Abrahamowicz, Robert W. Platt, Samy Suissa

Bibliographic record

VenueDiabetic Medicine · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism, Diabetes, and Cancer
Canadian institutionsRoyal Victoria HospitalMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsObservational studyMedicinePioglitazoneMeta-analysisBladder cancerMEDLINEIncidence (geometry)Research designIntensive care medicineType 2 diabetesInternal medicineCancerDiabetes mellitusEndocrinologyStatistics

Abstract

fetched live from OpenAlex

AIM: To conduct a systematic review of all observational studies on the effect of pioglitazone on the risk of bladder cancer. METHODS: The MEDLINE and EMBASE databases were queried for papers published between 1 January 2000 and 30 October 2017. We took into consideration observational studies (both retrospective and prospective) that included participants with Type 2 diabetes prescribed anti-hyperglycaemic drugs. RESULTS: While some studies reported an association, others did not, and meta-analyses of these studies showed a significantly increased risk; however, while meta-analysis is a powerful and practical statistical tool, its results should be considered with caution when applied to widely heterogeneous studies. We describe how many of these studies are affected by different types of bias, most notably time-related biases, which should preclude a pooled analysis that would result in biased estimation of the risk. CONCLUSIONS: Given existing data, it is not appropriate to pool the outcomes of highly heterogeneous studies and further rigorously conducted observational research is needed to clarify the role of pioglitazone use on the incidence of bladder cancer.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.359
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.117
GPT teacher head0.378
Teacher spread0.260 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations34
Published2018
Admission routes1
Has abstractyes

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