Rationale and Approach to Evaluating Interventions for Newborn Care in Low- and Middle-Income Countries
Notice bibliographique
Résumé
INTRODUCTION: The neonatal period is the most vulnerable time in a child's life, contributing to almost half of all deaths in children under 5 years. Many of these deaths are preventable and are mainly caused by preterm birth, birth asphyxia, or serious infections. Over the past decade, the evidence base for interventions to prevent and manage these causes of neonatal mortality and morbidity in low- and middle-income countries (LMICs) has expanded significantly. This growth calls for a comprehensive and systematic approach to synthesizing the available evidence. This paper describes the methodological approach taken before and during the conduct of the systematic overviews and reviews described in the online supplementary material (for all online suppl. material, see https://doi.org/10.1159/000542754 ). METHODS: Alongside consultation with a newborn technical advisory group, the overall evidence synthesis approach began with an extensive literature-scoping exercise to establish a universe of interventions that were relevant to neonatal health and survival and to identify the associated systematic reviews examining their effectiveness. Three main approaches were taken to synthesize the evidence based on the availability of prior evidence. New systematic reviews were conducted for topics lacking an existing comprehensive synthesis. Existing systematic reviews with search dates prior to 2020 were updated. High-quality, up-to-date systematic reviews were used without modification. In all cases, trial data from studies conducted in LMICs were sought and prioritized for analysis. CONCLUSION: A comprehensive approach to summarizing the best available evidence for newborn intervention effectiveness is described. INTRODUCTION: The neonatal period is the most vulnerable time in a child's life, contributing to almost half of all deaths in children under 5 years. Many of these deaths are preventable and are mainly caused by preterm birth, birth asphyxia, or serious infections. Over the past decade, the evidence base for interventions to prevent and manage these causes of neonatal mortality and morbidity in low- and middle-income countries (LMICs) has expanded significantly. This growth calls for a comprehensive and systematic approach to synthesizing the available evidence. This paper describes the methodological approach taken before and during the conduct of the systematic overviews and reviews described in the online supplementary material (for all online suppl. material, see https://doi.org/10.1159/000542754 ). METHODS: Alongside consultation with a newborn technical advisory group, the overall evidence synthesis approach began with an extensive literature-scoping exercise to establish a universe of interventions that were relevant to neonatal health and survival and to identify the associated systematic reviews examining their effectiveness. Three main approaches were taken to synthesize the evidence based on the availability of prior evidence. New systematic reviews were conducted for topics lacking an existing comprehensive synthesis. Existing systematic reviews with search dates prior to 2020 were updated. High-quality, up-to-date systematic reviews were used without modification. In all cases, trial data from studies conducted in LMICs were sought and prioritized for analysis. CONCLUSION: A comprehensive approach to summarizing the best available evidence for newborn intervention effectiveness is described.
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 | 0,348 | 0,395 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,007 | 0,013 |
| Bibliométrie | 0,018 | 0,013 |
| Études des sciences et des technologies | 0,004 | 0,011 |
| Communication savante | 0,011 | 0,009 |
| Science ouverte | 0,011 | 0,010 |
| Intégrité de la recherche | 0,013 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,006 |
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 ».