MétaCan
Menu
Back to cohort
Record W2017615708 · doi:10.1097/mou.0b013e328364f520

Epidemiology of male urinary incontinence

2013· review· en· W2017615708 on OpenAlexaff
Kari A.O. Tikkinen, Arnav Agarwal, Tomas L. Griebling

Bibliographic record

VenueCurrent Opinion in Urology · 2013
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsMcMaster University
FundersSuomen KulttuurirahastoSuomen Lääketieteen Säätiö
KeywordsMedicineUrinary incontinenceEpidemiologyUrinary systemPopulationQuality of life (healthcare)UrologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Urinary incontinence is a common clinical problem, particularly in older adults. This review was designed to review current and recently updated information on the epidemiology of urinary incontinence in men. RECENT FINDINGS: There are an increasing number of urinary incontinence studies outside Europe and North America. However, overall, the trend indicates no remarkable increase in the number of publications relative to all areas. Prevalence studies have produced estimates with very wide ranges, mainly due to methodological differences. Studies have also examined risk factors and impact of urinary incontinence, including lifestyle, comorbidities and medications. SUMMARY: Recent research has focused less on urinary incontinence in men compared with women, despite urinary incontinence being relatively common and burdensome in men worldwide and strongly associated with age. Thus, the population burden is likely to increase with future demographic shifts. A wide variety of risk factors have been identified, and studies have shown that urinary incontinence can have substantial negative impacts on various clinical outcomes and quality of life.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.162
GPT teacher head0.435
Teacher spread0.273 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in UrologySame topicPelvic floor disorders treatmentsFrench-language works237,207