Probiotic strategies for the treatment and prevention of bacterial vaginosis
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Urogenital infections are on average the number-one reason for women to visit the doctor. Yet, treatment and preventive strategies have gone unchanged for close to 50 years. With prevalence rates for bacterial vaginosis at more than 29%, depending on the population, and similarly high incidences of vulvo-vaginal candidiasis and urinary tract infections, plus HIV, new therapies are urgently needed to improve the health of women around the world. AREAS COVERED IN THE REVIEW: This review discusses the vaginal microbiota, our improved understanding of its composition, and its role in health and disease. It also discusses the progress made in the past 10 years or so, with the development and testing of probiotic lactobacilli to improve vaginal health and better manage urogenital infection recurrences. WHAT THE READER WILL GAIN: The reader will have an understanding of the clinical data obtained so far, and the potential mechanisms of action of probiotics. Despite the need for more clinical studies, the review illustrates a case for inclusion of probiotics as part of the approach to disease prevention, and as an adjunct to antimicrobial treatment. Challenges remain in optimizing clinical benefits, selecting new strains, preparing new products and having them tested in humans then approved with informative claims, and making products readily accessible to women in the developed and developing world. TAKE HOME MESSAGE: The vaginal microbiota is a complex structure that can change quickly and dramatically, and significantly impact a woman's health. New health-maintenance and disease-treatment approaches are badly needed, and probiotics should be considered.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".