Global challenges in the identification and management of pre-eclampsia
Notice bibliographique
Résumé
Although maternal mortality is thought to be declining, it is estimated that around 800 women die daily as a result of complications of pregnancy. 85% of this burden lies with Sub-Saharan Africa and Southern Asia and all ten of the countries with the highest maternal mortality ratios are in Africa [1]. Pre-eclampsia (defined as hypertension and proteinuria occurring after 20 weeks? gestation of pregnancy) [2] is a leading cause of maternal morbidity and mortality in the developing world, and is also associated with poor fetal outcomes, including fetal growth restriction, and intrauterine fetal death [3]. The World Health Organisation estimates that around 26% of severe maternal pregnancy outcomes are associated with pre-eclampsia [4], and it is thought to be responsible for approximately 40,000 maternal deaths annually [5]. 99% of these deaths occur in developing countries [1]. There are a number of factors and challenges contributing to the stark disparity in pre-eclampsia related morbidity and mortality in the developing world, compared to the developed world. Restricted or under-staffed maternity services, particularly in rural areas with poor transport systems, mean that women in low and middle-income countries may present late or infrequently to antenatal care where pre-eclampsia (a frequently asymptomatic condition) may be diagnosed. However, even when uptake of antenatal healthcare is high, pre-eclampsia remains under-diagnosed. Early and accurate identification of at-risk women depends on regular and accurate blood pressure monitoring [6-8]. However, training in the use of the technically challenging sphygmanometer may be insufficient, and equipment may be lacking or defective [7]. Detection of proteinuria by dipstick has low specificity and depends on a regular supply of accurate urine dipsticks. Once pre-eclampsia is diagnosed (often at a late stage), skilled management is essential (timely delivery of the infant with skilled attendance, preceded by blood pressure control and seizure prevention). However, access to such care may be restricted by transport limitations and referral options, particularly for poor women in rural settings. Once care has been accessed, the quality may be sub-standard. For example, although magnesium sulphate has been shown to be effective for the prevention and control of eclamptic seizures [2] it is yet to be widely used in many low and middle income countries [4]. Efforts to reduce maternal morbidity and mortality due to the complications of preeclampsia in low and middle income countries must be based around improving detection of pre-eclampsia in community and primary care settings, accompanied by improved access to higher-level facilities which can safely induce labour or deliver by caesarean section, with the availability of appropriate high quality medications for blood pressure control and seizure prophylaxis.
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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 | 0,006 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
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 ».