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Enregistrement W3193007179 · doi:10.1093/humrep/deab130.392

P–393 The relationship of cigarette smoking with gestational diabetes. An evaluation of a database of more than nine million deliveries

2021· article· en· W3193007179 sur OpenAlexaff
Ido Feferkorn, Ahmad Badeghiesh, H Badeghiesh, Michael H. Dahan

Notice bibliographique

RevueHuman Reproduction · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueGestational Diabetes Research and Management
Établissements canadiensUniversity of TorontoMcGill University
Organismes subventionnairesnon disponible
Mots-clésMedicinePregnancyGestational diabetesConfoundingHealthcare Cost and Utilization ProjectPopulationObstetricsRetrospective cohort studyDiabetes mellitusDemographyGynecologyEnvironmental healthGestationHealth careInternal medicineEndocrinology

Résumé

récupéré en direct d'OpenAlex

Abstract Study question Given the common pathophysiology between type 2 DM (risk of which is increased by smoking) and GDM we sought to assess whether an association between smoking and GDM exists? Summary answer After controlling for confounding effects, women who smoke during pregnancy are at an increased risk of developing GDM. What is known already Smoking is well associated with type 2 diabetes mellitus (DM) in multiple studies. It has remained unclear whether there is also an association between smoking and GDM as publications report conflicting results. In a meta-analysis of 1,364,468 pregnancies (22,811 smokers) there was no association between cigarette smoking and the risk of GDM. While a study from the Pregnancy Risk Assessment Monitoring System, on 222,408 patients (54,114 smoked during pregnancy) found a higher risk for GDM among smokers. Study design, size, duration A retrospective population-based study utilizing data from the Healthcare Cost and Utilization Project—Nationwide Inpatient Sample (HCUP-NIS). A dataset of all deliveries between 2004 and 2014 inclusively, was created. Within this group, all deliveries to women who smoked during pregnancy were identified as part of the study group (n = 443,590), and the remaining deliveries were categorized as non smoker births and comprised the reference group (n = 8,653,198). Participants/materials, setting, methods The HCUP-NIS is the largest inpatient sample database in the USA, and it is comprised of hospitalizations throughout the country. It provides information relating to 20% of US admissions and represents over 96% of the American population. Multivariate logistic regression analysis, controlling for confounding effects, was conducted to explore associations between smoking and delivery and neonatal outcomes. According to Tri-Council Policy statement (2018), IRB approval was not required, given data was anonymous and publicly available. Main results and the role of chance Our study identified 9,096,788 births between 2004–2014, of which 443,590 (4.8%) had a documented diagnosis of maternal smoking. Smokers were more likely to be young (53% vs 37.2% under the age of 35), white (78% vs 51.1%), of lower income (39.1% vs 26.6%), delivered in a rural hospital (28.7% vs 13.2%), suffer from obesity (6.4% vs 3.4%), have pregestational diabetes (1.2% vs 0.9%) and chronic hypertension (2.5% vs 1.8%) and to have undergone a previous caesarean section (17.7% vs 5.9%) (all p value <0.0001, all were controlled for in the logistic regression analysis). An increased risk for GDM among smokers was detected with an adjusted odds ratio (aOR) of 1.10 (95%CI:1.07–1.14 p < 0.0001), when controlling for the factors above. A significant higher risk of preterm delivery (aOR1.39, 95%CI:1.35–1.43, p < 0.0001), PPROM (aOR 1.52 ,95%CI:1.43–1.62, p < 0.0001), wound complications (aOR1.24,95%CI:1.09–1.41, p < 0.0001), and the need for hysterectomy (aOR1.32,95%CI:1.0–1.64,p< 0.0001) among the smokers was found as well. Limitations, reasons for caution The limitations of our study are its retrospective nature and the fact that it relies on an administrative database. Wider implications of the findings: The public health implications of confirming smoking as a risk for GDM are many. This can lead to earlier screening in pregnancy of smokers for GDM. The earlier initiation of interventions could decrease fetal complications and possibly have impact on the life and long-term health of that offspring. Trial registration number Not applicable

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,128
Score d'incertitude au seuil0,247

Scores Codex et Gemma par catégorie

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

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 Opus0,096
Tête enseignante GPT0,352
Écart entre enseignants0,255 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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
Publié2021
Routes d'admission1
Résumé présentoui

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