Influence of race/ethnicity on prevalence and presentation of endometriosis: a systematic review and meta‐analysis
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it