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Record W4387493227 · doi:10.1186/s12301-023-00383-1

Risk factors of stress urinary incontinence in pelvic organ prolapse patients: a systematic review and meta-analysis

2023· review· en· W4387493227 on OpenAlexaboutno aff
Andiva Nurul Fitri, Eighty Mardiyan Kurniawati, Sundari Indah Wiyasihati, Citrawati Dyah Kencono Wungu

Bibliographic record

VenueAfrican Journal of Urology · 2023
Typereview
Languageen
FieldMedicine
TopicPelvic floor disorders treatments
Canadian institutionsnot available
FundersUniversitas Airlangga
KeywordsMedicineUrinary incontinenceStress incontinenceDiabetes mellitusGynecologyMeta-analysisUrinary systemQuality of life (healthcare)UrologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Stress urinary incontinence (SUI) and pelvic organ prolapse (POP) commonly coexist as global problems that affect the quality of life of millions of women. The study aimed to identify the risk factors of stress urinary incontinence in pelvic organ prolapse patients. Main body A systematic review and meta-analysis was conducted in Web of Science, PubMed, and Scopus based on the PRISMA flowchart. The quality of the study was assessed using Newcastle–Ottawa Scale and data were collected on a modified table from The Cochrane Library. Meta-analysis was conducted using RevMan 5.4. Seven hundred forty studies were found that matched the keywords. After the screening, 16 studies met the inclusion and exclusion criteria with a total of 47.615 participants with pelvic organ prolapse. A total of 27 risk factors were found in this review. History of hysterectomy (OR = 2.01; 95% CI 1.22–3.33; p = 0.007), obesity (OR = 1.15; 95% CI 1.02–1.29; p = 0.02), and diabetes mellitus (OR = 1.85; 95% CI 1.06–3.23; p = 0.03) were shown to be risk factor of stress urinary incontinence in pelvic organ prolapse patients. Conclusions History of hysterectomy, obesity, and diabetes mellitus were found to be the risk factors of stress urinary incontinence in pelvic organ prolapse patients.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.002
Bibliometrics0.0020.002
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 head0.044
GPT teacher head0.320
Teacher spread0.276 · 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 designMeta-analysis
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

Citations4
Published2023
Admission routes1
Has abstractyes

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