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Record W2587921420 · doi:10.5489/cuaj.4337

Overactive bladder in children

2017· review· en· W2587921420 on OpenAlexaffvenue
Sophie Ramsay, Stéphane Bolduc

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typereview
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOveractive bladderUrologyMedicine

Abstract

fetched live from OpenAlex

Overactive bladder (OAB) is a highly prevalent disorder in the pediatric population. This condition is especially troublesome for pediatric patients and their families when associated with incontinence, since it negatively affects self-esteem and impairs children's development. From the patient's perspective, urgency and urge incontinence can have a significant impact, negatively affecting their quality of life. For a therapy to have true benefit, changes must not only be statistically significant, but must also be perceived as meaningful by the patient. A stepwise approach is favoured to treat this pathology, starting with behavioural therapy, followed by medical management, and eventually more invasive procedures. Antimuscarinic agents are the mainstay of medical treatment for OAB. Oxybutynin is the most commonly used antimuscarinic in the pediatric population. However, some patients have a suboptimal response to antimuscarinics and many experience bothersome side effects, which have been documented with all antimuscarinics to a significantly higher degree than placebo. Although there have been reports about the use of tolterodine, fesoterodine, trospium, propiverine, and solifenacin in children, to date, only oxybutynin has been officially approved for pediatric use by medical authorities in North America. This review will address alternative treatment options for pediatric patients presenting with OAB, from conservative measures to more invasive therapies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.883
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.074
GPT teacher head0.376
Teacher spread0.303 · 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.

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

Citations48
Published2017
Admission routes2
Has abstractyes

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