Need for more evidence in the prevention and management of perinatal asphyxia and neonatal encephalopathy in low and middle-income countries: A call for action
Bibliographic record
Abstract
Although low- and middle-income countries (LMICs) shoulder 90 % of the neonatal encephalopathy (NE) burden, there is very little evidence base for prevention or management of this condition in these settings. A variety of antenatal factors including socio-economic deprivation, undernutrition and sub optimal antenatal and intrapartum care increase the risk of NE, although little is known about the underlying mechanisms. Implementing interventions based on the evidence from high-income countries to LMICs, may cause more harm than benefit as shown by the increased mortality and lack of neuroprotection with cooling therapy in the hypothermia for moderate or severe NE in low and middle-income countries (HELIX) trial. Pooled data from pilot trials suggest that erythropoietin monotherapy reduces death and disability in LMICs, but this needs further evaluation in clinical trials. Careful attention to supportive care, including avoiding hyperoxia, hypocarbia, hypoglycemia, and hyperthermia, are likely to improve outcomes until specific neuroprotective or neurorestorative therapies available.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".