Intricate Connections between the Microbiota and Endometriosis
Bibliographic record
Abstract
Imbalances in gut and reproductive tract microbiota composition, known as dysbiosis, disrupt normal immune function, leading to the elevation of proinflammatory cytokines, compromised immunosurveillance and altered immune cell profiles, all of which may contribute to the pathogenesis of endometriosis. Over time, this immune dysregulation can progress into a chronic state of inflammation, creating an environment conducive to increased adhesion and angiogenesis, which may drive the vicious cycle of endometriosis onset and progression. Recent studies have demonstrated both the ability of endometriosis to induce microbiota changes, and the ability of antibiotics to treat endometriosis. Endometriotic microbiotas have been consistently associated with diminished Lactobacillus dominance, as well as the elevated abundance of bacterial vaginosis-related bacteria and other opportunistic pathogens. Possible explanations for the implications of dysbiosis in endometriosis include the Bacterial Contamination Theory and immune activation, cytokine-impaired gut function, altered estrogen metabolism and signaling, and aberrant progenitor and stem-cell homeostasis. Although preliminary, antibiotic and probiotic treatments have demonstrated efficacy in treating endometriosis, and female reproductive tract (FRT) microbiota sampling has successfully predicted disease risk and stage. Future research should aim to characterize the “core” upper FRT microbiota and elucidate mechanisms behind the relationship between the microbiota and endometriosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".