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Record W4200265772 · doi:10.5213/inj.2142146.073

The Relationship Between Lower Urinary Tract Symptoms and Osteoarthritis Symptoms Among Vendors in a Conventional Market

2021· article· en· W4200265772 on OpenAlexaboutno aff
Hyo Jeong Song, Maria Danet Lapiz Bluhm, Moonju Lee, Hyung Jee Kim, Hong Sang Moon

Bibliographic record

VenueInternational Neurourology Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
FundersJeju National University
KeywordsMedicineLower urinary tract symptomsInternational Prostate Symptom ScoreOsteoarthritisWOMACPhysical therapyUrinary systemUrinary incontinenceInternal medicineCross-sectional studyUrologyProstateAlternative medicinePathology

Abstract

fetched live from OpenAlex

PURPOSE: This study aimed to investigate lower urinary tract symptoms (LUTS) and the correlation between LUTS and osteoarthritis (OA) symptoms in the vendors working in a conventional market. METHODS: This cross-sectional study was conducted on 153 vendors aged 40 and over from August 10th to September 8th, 2020, in a conventional market. Data were collected via the self-reported questionnaires. We assessed LUTS by International Prostate Symptom Score (IPSS) and OA symptoms by Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: The mean age of 153 subjects was 61.31±9.92 years old. The mean score of IPSS and WOMAC was 5.37±5.68 (range, 0-35) and 16.89±19.61 (range, 0-96). Fifty-one percent of subjects had urinary incontinence at least monthly. Twenty-four point two percent of subjects had moderate-to-severe LUTS which were defined as a score of IPSS ≥8. LUTS were positively correlated with OA symptoms (r=0.41, P<0.001). CONCLUSION: The results showed that LUTS were associated with OA symptoms, and it also emphasized the need for vendors to be provided with a health education program to manage and prevent their LUTS and OA symptoms.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.014
GPT teacher head0.275
Teacher spread0.261 · 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.

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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