Influence of race/ethnicity on prevalence and presentation of endometriosis: a systematic review and meta‐analysis
Bibliographic record
Abstract
Background Understanding the impact of race/ethnicity on the prevalence and presentation of endometriosis may help improve patient care. Objective To review systematically the evidence for the influence of race/ethnicity on the prevalence of endometriosis. Search strategy CENTRAL , MEDLINE, PubMed, Embase, LILACS , SCIELO , and CINAHL databases, as well as the grey literature, were searched from date of inception until September 2017. Selection criteria Randomised control trials and observational studies reporting on prevalence and/or clinical presentation of endometriosis. Data collection and analysis Twenty studies were included in the review and 18 studies were used to calculate odds ratio ( OR ) with 95% confidence interval (CI) through a random effects model. Methodological quality was assessed using the Newcastle‐Ottawa risk of bias scale ( NOS ). Main results Compared with White women, Black woman were less likely to be diagnosed with endometriosis ( OR 0.49, 95% CI 0.29–0.83), whereas Asian women were more likely to have this diagnosis ( OR 1.63, 95% CI 1.03–2.58). Compared with White women, there was a statistically significant difference in likelihood of endometriosis diagnosis in Hispanic women ( OR 0.46, 95% CI 0.14–1.50). Significant heterogeneity ( I 2 > 50%) was present in the analysis for all racial/ethnic groups but was partially reduced in subgroup analysis by clinical presentation, particularly when endometriosis was diagnosed as self‐reported, Conclusions Prevalence of endometriosis appears to be influenced by race/ethnicity. Most notably, Black women appear less likely to be diagnosed with endometriosis compared with White women. There is scarce literature exploring the influence of race/ethnicity on symptomatology, as well as treatment access, preference, and response. Tweetable abstract Prevalence of endometriosis may be influenced by race/ethnicity, but there is limited quality literature exploring this topic.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".