Setting Research Priorities to Reduce Almost One Million Deaths from Birth Asphyxia by 2015
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 it