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Record W192051515 · doi:10.31436/imjm.v10i1.700

Prevalence of lower urinary tract symptoms (LUTS) among young age medical population

2011· article· en· W192051515 on OpenAlexaff
N Zalina, N Aruku, Nor Azura, N Shahida, N Akhmarina, F Dian

Bibliographic record

VenueIIUM Medical Journal Malaysia · 2011
Typearticle
Languageen
FieldMedicine
TopicUrinary Tract Infections Management
Canadian institutionsRoyal College of Physicians and Surgeons of Canada
Fundersnot available
KeywordsMedicineLower urinary tract symptomsUrinary incontinenceUrinary systemPopulationCross-sectional studyGynecologyObstetricsInternal medicineUrologyProstateEnvironmental health

Abstract

fetched live from OpenAlex

Introduction: Frequency of lower urinary tract symptoms (LUTS) in young age women is not well studied. It is said to be common among female elderly and multiparous population. The aim of this study is to obtain the prevalence of LUTS among nulliparous students in relation to their personal hygiene. Materials and methods: This is a prospective cross-sectional study conducted among 200 nulliparous medical and nursing students aged between 18-28 years using standardized questionnaires. Urine samples were also collected from students to detect urinary tract infections. Results: The complete data sets of 146 students were analyzed. All of them were nulliparous, single and not sexually active. The prevalence of LUTS was 52.7% consist of over-active bladder, urinary incontinence (UI) and voiding difficulty respectively (51.3%, 34.9% and 45.2%). The most common type of UI was stress urinary incontinence which was 21.9% followed by 11.6% of urgency incontinence. Conclusion: The prevalence of LUTS among young age population is high at 52.7%. Public awareness regarding LUTS and availability of treatment is needed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0160.000

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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 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

Citations9
Published2011
Admission routes1
Has abstractyes

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