Bibliographic record
Abstract
Endometriosis is a chronic condition that affects ˜10% of young women worldwide. Pain and infertility are the two most common features of the disease. The condition appears to be sex hormone-dependent, although a subset of females with the condition still experience symptoms post-menopause. The aetiology of endometriosis induction still remains elusive, and surgery to remove the lesions often fails to cure the condition, as the lesions often reappear. The lesions contain stromal cells, blood vessels, nerves, and numerous mast cells. In some respects, endometrial lesions resemble a chronic fibrotic scar-like tissue that does not resolve. Studies in other fibrotic abnormal healing conditions have revealed that targeting mast cells, as a central component of what is called a ‘neural–mast cell–fibroblast’ axis, by repurposing asthma drugs can prevent induction of the abnormal healing phenotype. Given the similarities between conditions with abnormal healing phenotypes and endometrial lesions, it is postulated that taking a similar approach to target endometrial lesion mast cells could exert a benefit for patients with endometriosis. This review also outlines approaches to assess the likelihood that targeting mast cells could lead to clinical trials using such ‘repurposed’ mast cell targeted drugs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".