Should hormone replacement therapy (any route of administration) be considered in all postmenopausal women with lower urinary tract symptoms? Report from the ICI‐RS 2023
Bibliographic record
Abstract
AIMS: This International Consultation on Incontinence-Research Society report aims to summarize the evidence and uncertainties regarding the use of hormone replacement therapy by any route in the management of lower urinary tract symptoms (LUTS) including recurrent urinary tract infections (rUTI), with a review of special considerations for the elderly. Research question proposals to further this field have been highlighted. METHODS: An overview of the existing evidence, guidelines, and consensus regarding the use of topical or systemic estrogens in the management of LUTS. RESULTS: There are currently evidence and recommendations to offer topical estrogens to postmenopausal women with overactive bladder symptoms as well as postmenopausal women with rUTIs. Systemic estrogens however have been shown in a meta-analysis to have a negative effect on LUTS and, therefore are not currently recommended. CONCLUSIONS: Although available evidence and recommendations exist for the use of topical estrogens, few women are commenced on these in primary care. There remain large gaps still within our knowledge of the use of estrogens within the management of LUTS, particularly on when it should be commenced, the length of time treatment should be continued for, and barriers to prescribing.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".