Setting research priorities to reduce global mortality from preterm birth and low birth weight by 2015
Bibliographic record
Abstract
AIM: This paper aims to identify health research priorities that could improve the rate of progress in reducing global neonatal mortality from preterm birth and low birth weight (PB/LBW), as set out in the UN's Millennium Development Goal 4. METHODS: We applied the Child Health and Nutrition Research Initiative (CHNRI) methodology for setting priorities in health research investments. In the process coordinated by the World Health Organization in 2007-2008, 21 researchers with interest in child, maternal and newborn health suggested 82 research ideas that spanned across the broad spectrum of epidemiological research, health policy and systems research, improvement of existing interventions and development of new interventions. The 82 research questions were then assessed for answerability, effectiveness, deliverability, maximum potential for mortality reduction and the effect on equity using the CHNRI method. RESULTS: The top 10 identified research priorities were dominated by health systems and policy research questions (eg, identification of LBW infants born at home within 24-48 hours of birth for additional care; approaches to improve quality of care of LBW infants in health facilities; identification of barriers to optimal home care practices including care seeking; and approaches to increase the use of antenatal corticosteriods in preterm labor and to improve access to hospital care for LBW infants). These were followed by priorities for improvement of the existing interventions (eg, early initiation of breastfeeding, including feeding mode and techniques for those unable to suckle directly from the breast; improved cord care, such as chlorhexidine application; and alternative methods to Kangaroo Mother Care (KMC) to keep LBW infants warm in community settings). The highest-ranked epidemiological question suggested improving criteria for identifying LBW infants who need to be cared for in a hospital. Among the new interventions, the greatest support was shown for the development of new simple and effective interventions for providing thermal care to LBW infants, if KMC is not acceptable to the mother. CONCLUSION: The context for this exercise was set within the MDG4, requiring an urgent and rapid progress in mortality reduction from low birth weight, rather than identifying long-term strategic solutions of the greatest potential. In a short-term context, the health policy and systems research to improve access and coverage by the existing interventions, coupled with further research to improve effectiveness, deliverability and acceptance of existing interventions, and epidemiological research to address the key gaps in knowledge, were all highlighted as research priorities.
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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 | 0.067 | 0.082 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".