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Record W4403129025 · doi:10.3390/ijms251910704

A Brief Overview of Cholinergic and Phosphodiesterase-5 Inhibitors in Diabetic Bladder Dysfunction

2024· review· en· W4403129025 on OpenAlexaff
Georgios Kallinikas, Georgios Haronis, Eirini Kallinika, Diomidis Kozyrakis, Evangelos Rodinos, Athanasios Filios, Panagiotis Filios, Despoina Mityliniou, Konstantinos Safioleas, Anastasios Zarkadas, Dimitrios Bozios, Athanasios Karmogiannis, Vasileios Konstantinopoulos, Anna Maria Konomi, Amin M. Ektesabi, James N. Tsoporis

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2024
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicinePhosphodiesteraseCholinergiccGMP-specific phosphodiesterase type 5Urinary incontinenceUrinary systemIntensive care medicinePathophysiologyBioinformaticsPharmacologyInternal medicineUrologyErectile dysfunctionBiologyEnzyme

Abstract

fetched live from OpenAlex

Diabetic bladder dysfunction (DBD) comprises a wide spectrum of lower urinary tract symptoms that impact diabetic patients' lives, including urinary frequency, urgency, incontinence, and incomplete bladder emptying. To relieve symptoms, anticholinergics have been widely prescribed and are considered an effective treatment. There is increasing evidence that diabetic patients may benefit from the use of phosphodiesterase 5 (PDE5) inhibitors. This narrative review aims to provide a brief overview of the pathophysiology of DBD along with a focus on cholinergic and phosphodiesterase inhibitors as therapies that benefit DBD. An examination of the literature suggests compelling avenues of research and underscores critical gaps in understanding the mechanisms underlying DBD. New tools and models, especially rodent models, are required to further elucidate the mechanisms of action of current therapies in the treatment of DBS.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.002

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 head0.075
GPT teacher head0.414
Teacher spread0.339 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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