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Enregistrement W6958321314 · doi:10.6084/m9.figshare.21313894

Postpartum hemorrhage drills or simulations and adverse outcomes: a systematic review and Bayesian meta-analysis

2022· article· en· W6958321314 sur OpenAlexaboutno aff

Notice bibliographique

RevueFigshare · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCultural and Educational Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBlood transfusionAdverse effectIntensive care unitPostpartum periodPregnancyCredible intervalBayesian probability

Résumé

récupéré en direct d'OpenAlex

To compare the rates of adverse outcomes with postpartum hemorrhage (PPH) before and after implementation of drills or simulation exercises We included all English studies that reported on rates of PPH and associated complications during the pre- and post-implementation of interventional exercises. Two investigators independently reviewed the abstracts, and full articles for eligibility of all studies. Inconsistencies related to study evaluation or data extraction were resolved by a third author. The co-primary outcomes were the rate of PPH and of any transfusion; the secondary outcomes included admission to the intensive care unit (ICU), transfusion ≥ 4 units of packed red blood cells, hysterectomy, or maternal death. Study effects were combined by Bayesian meta-analysis and reported as risk ratios (RR) and 95% credible intervals (Cr). We reviewed 142 full length articles. Of these, 18 publications, with 355,060 deliveries—150,562 (42%) deliveries during the pre-intervention and 204,498 (57.6%) deliveries in the post-interventional period—were included in the meta-analysis. Using the Newcastle-Ottawa Scale, only three studies were considered good quality, and none of them were done in the US. The rate of PPH prior to intervention was 5.06% and 5.46% afterwards (RR 1.09, 95% CI 0.87–1.36; probability of reduction in the diagnosis being 21%). The likelihood of transfusion decreased from 1.68% in the pre-intervention to 1.27% in the post-intervention period (RR 0.80, 95% Cr 0.57–1.09). The overall probability of reduction in transfusion was 93%, albeit it varied among studies done in non-US countries (96%) versus in the US (23%). Transfusion of 4 units or more of blood occurred in 0.44% of deliveries before intervention and 0.37% afterwards (RR of 0.85, 95% CI 0.50–1.52), with the overall probability of reduction being 72% (76% probability of reduction in studies from non-US countries and 49% reduction with reports from the US). Surgical interventions to manage PPH, which was not reported in any US studies, occurred in 0.14% before intervention and 0.28% afterwards (RR 1.29; 95% CI 0.56–3.06; probability of reduction 27%). Admission to the ICU occurred in 0.10% before intervention and 0.08% subsequently (RR 0.92, 95% CI 0.58–1.43), with the overall probability of reduction being 65% (81% in studies from non-US countries and 27% from the study done in the US). Maternal death occurred in 0.17% in the pre-intervention period and 0.09% during the post-intervention (RR 0.62, 95% CI 0.33–1.05; probability of reduction 93% in studies from non-US countries and 82% in one study from the US). Interventions to reduce the sequelae of PPH are associated with decrease in adverse outcomes. The conclusion, however, ought not to be accepted reflexively for the US population. All of the studies on the topic done in the US are of poor quality and the associated probability of reduction in sequelae are consistently lower than those done in other countries. Since the putative benefits of PPH drills or simulation exercises are based on poor quality pre- and post-intervention trials, policies recommending them ought to be revisited.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,925
Score d'incertitude au seuil0,756

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,2540,000

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.

Tête enseignante Opus0,146
Tête enseignante GPT0,368
Écart entre enseignants0,222 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeMéta-analyse
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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