Rationale and Approach to Evaluating Interventions for Newborn Care in Low- and Middle-Income Countries
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.348 | 0.395 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".