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The Prevalence of LUTS in Men Aged ≥40 years based on the IPSS-Ina Questionnaire

2023· article· en· W4400364490 on OpenAlexaff
Wangga P. Lasandara, Muhammad Taufiqurrachman, Dimas Bintoro Kresna Yustisia Handoyo

Bibliographic record

VenueBrawijaya Journal of Urology. · 2023
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsRegina General Hospital
Fundersnot available
KeywordsMedicineGerontologyGynecology

Abstract

fetched live from OpenAlex

Objectives. LUTS (Lower Urinary Tract Symptoms) are several symptoms experienced by a person due to various pathologies that occur in the lower urinary tract. The prevalence of LUTS in ≥ 40-year-old men is high and many methods including IPSS have been made to measure LUTS. Unfortunately, the high prevalence is not followed by the number of patients who are seeking treatment for their LUTS.Methods. This observational cross-sectional study used 45 men aged ≥40 years who came to Tongauna Health Center. Each respondent was asked to fill out the IPSS questionnaire. Moreover, when the respondent has LUTS, he is also asked if he is ever looking for health care providers, where he consultations, and what the reason is if he is never looking for it.Result. Of 45 men, there are 37 men who have LUTS and only 8 asymptomatic men. Mild LUTS is the most common symptom with 22 respondents, followed by moderate with 11 respondents, and only 4 respondents who suffered from severe LUTS. Pleased (1) is the most respondent’s feel for their Quality of Life score. Only 7 respondents are ever looking for treatment, and the common reasons for them not getting treatment are their misperception about LUTS, economic factors, and attitude toward their illness.Conclusion. The number of LUTS patients≥40 years old male at Tongauna District is high, but their initiative to treat this symptom is still low due to many factors like knowledge, economic, and patient attitude.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.262
Teacher spread0.251 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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