Setting Research Priorities to Reduce Almost One Million Deaths from Birth Asphyxia by 2015
Bibliographic record
Abstract
Intrapartum-related neonatal deaths (previously called “birth asphyxia”) are the fifth most common cause of deaths among children under 5 years of age, accounting for an estimated 814,000 deaths each year, and also associated with significant morbidity, resulting in a burden of 42 million disability adjusted life years (DALYs). \n This paper uses a systematic process developed by the Child Health Nutrition Research Initiative (CHNRI) to define and rank research options to reduce mortality from intrapartum-related neonatal deaths by the year 2015, in order to advance Millennium Development Goal (MDG) 4 for child survival. \n A list of 61 research questions was developed and scored by 21 technical experts. The top one-third of the ranked research investment options was dominated by delivery (implementation) research, whilst discovery (basic science) questions were not ranked highly, especially for expected reduction of mortality and inequity in the short time to 2015. \n Among the top four research questions, two relate to generation of demand for facility care at birth with specific mechanisms (such as transport and communication schemes, or financial incentives and conditional cash transfers). The other two top ranked priorities relate to use of community cadres and the roles they might effectively play—for example, screening for complications or supportive transfer to facilities and companionship at birth. The highest ranked discovery question concerned the interaction of hypoxia and infection, and the highest ranked epidemiologic question addressed prediction of intrapartum hypoxic injury. \n This exercise highlights the need for current research investments to focus on studies most likely to result in accelerated progress towards MDG 4 and in the countries where the most deaths occur.
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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 | 0.175 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".