A Dermatologist’s Approach to Genitourinary Syndrome of Menopause
Bibliographic record
Abstract
BACKGROUND: Genitourinary syndrome of menopause (GSM) is a debilitating condition caused by hypoestrogenism that presents with vaginal dryness and dyspareunia as well as other genital, sexual, and urinary symptoms. Previously known as atrophic vaginitis, the term GSM is now used. OBJECTIVE: To help familiarise dermatologists with diagnosing and managing GSM. METHODS: In total, 218 articles were identified and reviewed by 2 independent authors using PubMed. Articles included were from December 2005 to December 2015. Sixty-seven articles met our inclusion criteria. RESULTS: GSM is a clinical diagnosis, requiring the presence of symptoms that should be bothersome and not accounted for by another condition. A pH test may help with diagnosis as vaginal pH will be increased from acidic to neutral. The Papanicolaou test is not recommended because of poor clinical correlation. First-line treatment is low-dose local vaginal estrogen therapy, which has proven efficacy and safety. Serum estrogen levels are not significantly affected with the exception of creams containing high-dose conjugated equine estrogens. Other options have yet to be approved for use in Canada but show promise. CONCLUSION: GSM is a debilitating and common condition that suffers from barriers to diagnosis and treatment. Current treatments are well tolerated, rewarding, and effective with rapid onset.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".