Editorial: Technologies for neonatal care in LMICs
Bibliographic record
Abstract
Newborn babies are among the most vulnerable class of patients in any society. They are entirely incapable of surviving on their own without external help from caregivers and society. A poorly attended newborn will more likely die compared to another who received well-guided and knowledgeable care [1]. Therefore, the neonatal mortality rate of any society represents a quick measure of the efficiency of its healthcare system, available technologies, and knowledge base. It is common knowledge that the low-and middle-income countries (LMICs) disproportionately contribute over 98% of the global annual burden of neonatal deaths [2,3]. Limited access to sustainable affordable technologies for neonatal care is one of the major impediments in lowering neonatal mortality in LMICs [4]. Expensive medical equipment that works well in high income countries (HICs) may be unsustainable in LMICs due to poor operational infrastructure [5]. Thus making sophisticated technologies as applied in HICs both unaffordable and unsustainable in LMICs. However, a well-crafted basic technology, may be extremely affordable, easily maintainable by in-house technicians, and effective in saving lives.Therefore, we encouraged researchers to submit their practical demonstrations of applicable LMIC innovations to enable a Collection of crossbreed-able ideas for empowering the rest of the LMICs in neonatal care.Our Topic Collection has showcased ten rigorous research from 64 collaborating authors across many continents, drawing from easy-to-apply innovative technologies to address a variety of neonatal conditions. Singh et al. (India-Australia collaboration), explored the "diagnostic utility of lung ultrasound" in predicting when surfactant therapy is needed during neonatal respiratory support. They noted that lung pathologies for respiratory distress at birth have overlapping symptomatology with other conditions, hence the need to research the diagnostic accuracy of a cutoff for the lung ultrasound score (LUS) in predicting the need for surfactant therapy in neonatal respiratory distress. They corelated LUS and corresponding SPO2 to FiO2 in 100 neonates and found that LUS cutoff of 7 predicted the need for the first dose of surfactant.In another randomised controlled trial, Singh et al. compared the effect of Premature Infant Oral Motor Intervention (PIOMI) and routine oromotor stimulation (OMS) on oral feeding readiness.They concluded that PIOMI is a more effective oromotor stimulation method for improved oral feeding in preterm neonates. From the Republic of Korea, we have Hwang and Lee conduct a cross-sectional study, where safe-delivery kits were distributed to 534 mothers in Rural Ethiopian Communities to investigate the impacts this has on preventing newborn and maternal infection. The outcome demonstrates that single-use delivery kits decrease the likelihood of maternal infection, emphasizing the need for adoption in vulnerable countries to improve hygienic birthing, especially for deliveries outside healthcare facilities.Finally, a team of Nigeria-UK-Canada researchers-Amadi et al.-in their courtroom, "jury-style systematic review of 32 years of literature without significant mortality reduction", wondered why high neonatal-mortality-rate has persisted in Nigeria and some LMICs since the days of MDG_(4).They reviewed 4,286 publications but only 19 were assessed to possess potentials for reducing neonatal mortality, however, these remained largely unutilized by policymakers. Recommendation: LMIC healthcare systems may have to look inwards to strengthen identifiable game-changing discoveries they already possess.We invite organizations and policymakers of relevant countries to avail themselves of the rich contents of this Collection to implement a far-reaching neonatal life-saving campaign across LMICs-inspiring further research for inclusion in our next edition.Hippolite drafted the manuscript and contributed to its polishing and readiness. Tina, Olugbenga, and John contributed equally to the manuscript editing and polishing.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".