Prevalence of Microbial Isolates Cultured from Endometrial Swab Samples Collected from United Kingdom Thoroughbred Mares from 2014 to 2020
Bibliographic record
Abstract
Determining whether endometrial microbial isolates are pathogens, contaminants, or even part of the “normal” microbiome is extremely complex, particularly given the absence of “gold standard” tests for endometritis. Population-level benchmarking and temporal monitoring can provide novel insights and a wider context to improve understanding. This study aimed to (i) estimate the prevalence of endometrial isolates from swabs of Thoroughbred broodmares in Newmarket, UK between 2014 and 2020; and (ii) evaluate the effects of year, mare age, and cytology findings on isolate prevalence. Generalised linear mixed models with a logit link, both null models and models using year of sampling, mare age, or cytology findings as predictors, were fitted to estimate isolate prevalence. Over the 7-year period, data were available from 18,996 endometrial-swab samples from 6050 mares on 290 premises. The overall isolate prevalence was 35.5% (95% confidence interval (CI) 33.0–37.9), and this varied significantly between years. The most prevalent isolates were β-hemolytic Streptococcus (17.9; 95% CI: 17–19) and E. coli (10.3%; 95% CI: 9.0–11.6). Isolate prevalence increased with mare age except for E. coli isolates, and with increasing category of cytology findings except for α-hemolytic Streptococcus isolates. The results provide novel estimates of isolate prevalence and highlight knowledge gaps around potential complexities in the interpretation of findings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".