Differences Between 25-hydroxyvitamin D Levels in Patients with Pelvic Organ Prolapse and Non-Pelvic Organ Prolapse: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Treatment options for cases of pelvic organ prolapse still lead to the use of a pessary rather than a surgical method. Additional therapy is needed to help treat or prevent pelvic organ prolapse. Vitamin D deficiency has consistently been associated with decreased muscle function, so it is assumed that it will affect the pelvic floor muscles. This paper systematically explores the differences between 25-hydroxyvitamin D levels in patients with pelvic organ prolapse and non-pelvic organ prolapse. STUDY DESIGN: A systematic review was conducted through the PubMed, Google Scholar, Cochrane Library, and ScienceDirect databases using relevant keywords. Articles published in the last 10 years-from 2012 to 2022-that were written in English, that discuss the status or effect of vitamin D on pelvic organ prolapse, and that focus on 25 OH-vitamin D were included in the review. RESULTS: In total, 717 articles were filtered but 8 articles met the criteria. A total of 1339 women with prolapse and without prolapse with ages ranging from 20 years to 78 years were included in the study. The studies found did not use the same standard threshold in determining deficiency status. Most studies have found that there are lower levels of vitamin D in women who have had pelvic organ prolapse. A total of 7 of 8 studies confirmed the comparison of vitamin D-25OH levels in women with pelvic organ prolapse and without pelvic organ prolapse at P < 0.05. CONCLUSIONS: There are differences between 25-hydroxyvitamin D levels in patients with pelvic organ prolapse and non-pelvic organ prolapse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".