Intestinal microflora provides biomarkers for infertile women with endometrial polyps
Bibliographic record
Abstract
Endometrial polyps (EPs) are related to infertility; however, there are no biomarkers for identification. We evaluated changes in the intestinal microflora to identify microflora-based biomarkers that may be useful for detecting EPs. Intestinal specimens were prospectively collected from 100 women: 25 infertile women with EPs (InfEP + group), 25 infertile women without EPs (InfEP- group), and 50 healthy women (Fertile group). The microbiota composition was analyzed using 16S ribosomal RNA gene amplification and the intestinal expression of selected human genes using quantitative reverse transcription polymerase chain reaction. The InfEP + group had higher proportions of Prevotella, Streptococcus, Fusobacterium, Fenollaria, and Porphyromonas than the InfEP- and Fertile groups, while the Fertile group had higher proportions of Faecalibacterium, Bacteroides, and Blautia. We constructed a microbial dysbiosis index based on the intestinal microbiota at the genus level as a predictive model. The most accurate model to predict the presence of EPs was that including the Fertile and InfEP + groups (area under the curve: 0.89, 95% confidence interval: 0.79–0.96). The InfEP- and Fertile groups had significant differences in microflora composition compared with the InfEP + group. The intestinal microflora may be a useful biomarker for identifying EPs in infertile women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".