Representation of Ethnic Minority Patients in Caesarean Section Clinical Trials: A systematic review and meta-analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,044 | 0,109 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,022 | 0,040 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».