WERF Endometriosis Phenome and Biobanking Harmonisation Project for Experimental Models in Endometriosis Research (EPHect-EM-Homologous): homologous rodent models
Bibliographic record
Abstract
In vivo models of endometriosis enable the discovery and preclinical testing of new therapies. Several rodent models of endometriosis exist, but a lack of harmonization impedes reproducibility and comparability of results among investigators. Homologous models are advantageous as they allow the contribution of the immune system/inflammation to be studied. We reviewed published homologous rodent models of endometriosis to develop standard operating procedures ('EPHect-EM-Homologous-SOPs') to guide and facilitate the choice and implementation of these models and harmonize documentation to enhance interpretation and comparability of results. The World Endometriosis Research Foundation (WERF) established an international working group of experts in models of endometriosis and formed a working sub-group to discuss homologous rodent models of endometriosis. A systematic literature review and detailed analysis of protocols was performed. The identified models have advantages and limitations regarding physiological relevance and utility. To harmonize key variables for endometriosis rodent models, the working group focused on species and animal strains, placement of ectopic tissue, uterine tissue volume, method of induction, hormonal status, and uterine tissue 'type'. A decision tree and recommendations on model use were developed for mice and rats to serve as guides for the use of harmonized EPHect-EM-Homologous-SOPs, experimental design, reporting standards, and research of question-dependent key variables. No 'ideal' homologous model of endometriosis was identified. The choice of model for specific research should be guided according to a best-fit strategy. Harmonization of SOPs, documentation, and reporting standards will improve replicability and translational applicability of studies and better highlight where de novo model creation is needed.
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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 | 0.095 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".