Resolved threatened preterm labour: an opportunity for reducing future prematurity?
Notice bibliographique
Résumé
Prematurity remains the most significant obstetrical complication with respect to long-term newborn morbidity. Over the last few years, there has been some progress made in the prediction and reduction of risk of preterm birth (PTB). Prediction has focused on identification of maternal characteristics that increase risk, on the evaluation of cervical length and the use of biomarkers [fibronectin, PAPP A, inhibin, alpha fetoprotein (αFP)]. Of the preventive strategies, the use of progesterones in women perceived to be at risk is now commonplace and cervical cerclage or cervical pessaries are being used in selected cases. Many of the maternal characteristics that increase the risk of PTB are well established. Paramount to date have been the long recognised risk factors of a prior PTB or of prior premature membrane rupture (PROM) (Ekwo et al. Obstet Gynecol 1992;80:166–72). However, somewhat surprisingly, little attention has previously been paid to those women, who in earlier pregnancies did not in fact give birth preterm or suffer PROM, but who did present with episodes of threatened preterm labour (TPL) or premature uterine contractions. That is precisely the focus of this present study (Cho et al. BJOG 2019;126:901–5). From a large population dataset, the authors have demonstrated that, as expected, PTB in a first pregnancy is a risk factor, with an odds ratio (OR) of 8.15, for PTB in the second pregnancy. However, TPL in a pregnancy delivering at term was also associated with PTB, with an OR of 2.21. Both prior PTB and TPL with a term delivery present almost equal risks for women to experience PTL in their subsequent pregnancy, even if that pregnancy proceeds to term [OR 3.83 versus 4.61]. Although having less of an impact than prior PTB, given the larger proportion of women experiencing PTL followed by a term delivery, identification of these women as being an at-risk group identified almost as many of the subsequent PTBs as did prior PTB (6.1 versus 8.7% of all PTBs). Despite this modest benefit in detecting women at risk of PTB, considering all women with a combination of PTB and TPL with term delivery to be at risk of PTB identified a cohort of the study population comprising 4.5% of women, in whom just under 15% of all subsequent PTBs occurred. The follow-on question is how effective our preventive strategies might be in this cohort of women. This offers an ideal opportunity for future study and ideally this should be undertaken before we extend the use of current therapies to include this population of women without supportive evidence to justify that approach. It might be argued that the variable data regarding the mainstays of current preventive strategies, e.g. progesterone therapy (Jarde et al. BJOG 2017;124:1163–73; Norman et al. Lancet 2016;387:2106–16), as well as cerclage, exists because of the extrapolation of indications and the empiric use of treatment strategies in cases where robust data justifying these approaches is lacking. None declared. Completed disclosure of interest form is available to view online as supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».