Representation of Ethnic Minority Patients in Caesarean Section Clinical Trials: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: Caesarean section (CS) is the most common surgery performed in the world and rates have steadily increased in the last two decades. There are reports of both higher rates of CS and a greater frequency of complications in people with various ethnic backgrounds. We aimed to assess the representation of ethnic minority groups in CS randomized controlled trials (RCT) in the USA and Canada and quantify the trial characteristics that are associated with reporting ethnicities in this systematic review and meta-analysis. Methods: RCTs that evaluated patients undergoing CS and published between 2002 and 2022 were included. We searched MEDLINE and EMBASE databases and Cochrane CENTRAL register in November 2022. Risk of bias was assessed using Cochrane Risk of Bias (RoB2) tool. Analysis was performed to determine the proportion of RCTs that represented ethnic minorities in CS RCTs and the trial characteristics associated with reporting of ethnicities of the participants. We also conducted a metaanalysis to determine the proportion of ethnic representation within those RCTs reporting ethnicity and the associated trial characteristics. Sub-group analysis and meta-regression were conducted to explore heterogeneity. Results: A total of 224 RCTs (42 from Canada, 182 from USA) including 61,475 participants were included in the current analysis. 42.41% of RCTs reported ethnicity that included White, Black, Latino, Asian, and others. None of the RCTs reported subgroup analyses based on ethnicities. Having external funding (p value<0.001) and obstetrics (e.g., post-partum hemorrhage/infection) related studies (p value<0.001) were more likely to report ethnicities as opposed to trials with internal funding and trials that focused on anesthesia and pain-related concerns. In alignment of this, we observed a higher likelihood of reporting ethnicities among studies that included surgical (53.73%) and non-surgical procedures (44.44%) such as therapy or educational intervention compared to those with pharmacological (31.45%) interventions (p<0.001). Unblinded (68.00%) and single-blinded (50.00%) studies more commonly reported as opposed to double-blinded studies (32.03%), which was found statistically significant (p<0.001). Sample size (p=0.485) were not associated with a greater likelihood of reporting ethnicity. Multi-sited study tended to report ethnicities (57.58% vs 43.86%); however, it was not found statistically significant (p=0.148). The pooled proportion of ethnic participants in the studies that reported ethnicity was 0.51 (95% CI 0.41, 0.62). Conclusion: There is underreporting of the ethnicity in the USA and Canada CS RCT trials and no mention of further subgroup analysis of findings based on ethnicities in the USA and Canadian CS RCTs. Future CS RCTs should focus on recruitment of ethnic minority groups to ensure external generalizability. In addition, if feasible, subgroup analyses based on ethnicity should be conducted to identify if differences in treatment efficacy and safety exist in patients undergoing CS.
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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 | 0.044 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".