The case of the neonate vs. LMIC medical academia—a jury-style systematic review of 32 years of literature without significant mortality reduction
Bibliographic record
Abstract
Introduction: The high neonatal mortality rate in low- and middle-income countries (LMICs) such as Nigeria has lasted for more than 30 years to date with associated nursing fatigue. Despite prominent hard work, technological improvements, and many publications released from the country since 1990, the problem has persisted, perhaps due to a lack of intervention scale-up. Could there be neglected discoveries unwittingly abandoned by Nigerian policymakers over the years, perhaps locked up in previous publications? A careful review may reveal these insights to alert policymakers, inspire researchers, and refocus in-country research efforts towards impactful directions for improving neonatal survival rates. The focus was to determine the prevailed effectiveness of LMIC medical academia in creating solutions to end the high neonatal mortality rate. Methods: An unconventional systematic review protocol structure following the PRISMA 2020 checklist was designed and registered at INPLASY (registration number: INPLASY202380096, doi: 10.37766/inplasy2023.8.0096). A jury of paediatricians was assembled and observed by a team of legal professionals. The jury searched the literature from 1990 to the end of 2022, extracted newborn-related articles about Nigeria, and assessed and debated them against expected criteria for solution creation, translation, scale-up, sustainability, and national coverage. Each juror used preset criteria to produce a verdict on the possibility of a published novel idea being a potential game-changer for improving the survival rate of Nigerian neonates. Results: A summation of the results showed that 19 out of 4,286 publications were assessed to possess potential strategies or interventions to reduce neonatal mortality. Fourteen were fully developed but not appropriately scaled up across the country, hence denying neonates proper access to these interventions. Conclusion: Nigeria may already have the required game-changing ideas to strategically scale up across the nation to accelerate neonatal survival. Therefore, LMIC healthcare systems may have to look inward to strengthen what they already possess. Systematic Review Registration: https://inplasy.com/, identifier (INPLASY202380096).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".