Customized birth‐weight centiles and placenta‐related fetal growth restriction
Notice bibliographique
Résumé
ABSTRACT Objective The value of using customized birth‐weight centiles to improve the diagnostic accuracy for fetal growth restriction (FGR), in comparison with using population‐based charts, remains a matter of debate. One potential explanation for the conflicting data is that most studies used measures of perinatal mortality and morbidity as proxies for placenta‐mediated FGR, many of which are not specific and may be confounded by other factors such as prematurity. The aim of this study was to compare the diagnostic accuracy of small‐for‐gestational age (SGA) at birth, defined according to customized vs population‐based charts, for associated abnormal placental pathology. Methods This was a secondary analysis of data from a prospective cohort study on risk factors for placenta‐mediated complications and abnormal placental pathology in low‐risk nulliparous women. All placentae were sent for detailed histopathological examination by two perinatal pathologists. The primary exposure was SGA, defined as birth weight < 10th centile for gestational age using either a customized (SGAcust) or a population‐based (SGApop) birth‐weight reference. The outcomes of interest were one of three types of abnormal placental pathology associated with FGR: maternal vascular malperfusion (MVM), chronic villitis and fetal vascular malperfusion (FVM). Adjusted relative risks (aRR) with 95% CIs were estimated using modified Poisson regression analysis, with adjustment for smoking, body mass index and aspirin treatment. Results A total of 857 nulliparous women met the study criteria. The proportions of infants identified as SGA based on the customized and population‐based charts were 12.6% (108/857) and 11.4% (98/857), respectively. A diagnosis of SGA using either customized or population‐based charts was associated with an increased risk of any placental pathology (aRR, 3.04 (95% CI, 2.29–4.04) and 1.60 (95% CI, 1.10–2.31), respectively) and MVM pathology (aRR, 12.33 (95% CI, 6.60–23.03) and 5.29 (95% CI, 2.87–9.76), respectively). SGAcust, but not SGApop, was also associated with an increased risk for chronic villitis (aRR, 1.85 (95% CI, 1.07–3.18)) and FVM pathology (aRR, 2.48 (95% CI, 1.25–4.93)). SGAcust had a higher detection rate for any placental pathology (30.3% vs 17.1%; P < 0.001), MVM pathology (63.2% vs 39.5%; P = 0.003) and chronic villitis (20.8% vs 8.3%; P = 0.007) than did SGApop, for a similar false‐positive rate. This was mainly the result of a higher detection rate for abnormal pathology in the white and East‐Asian subgroups and a lower false‐positive rate for abnormal pathology in the South‐Asian subgroup by SGAcust than by SGApop. In addition, pregnancies in the SGAcust group, but not those in the SGApop group, were more likely to be complicated by preterm birth and a low 5‐min Apgar score than were the corresponding non‐SGA group. Conclusion These findings suggest that customized birth‐weight centiles may be superior to population‐based birth‐weight centiles in detecting FGR that is due to underlying placental disease. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.
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 | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».