Reducing Early Neonatal Heat Loss in a Low Resourced Context: An Indian Exemplar
Bibliographic record
Abstract
Background Although there has been a favorable trend in the Infant Mortality Rate in India in the last decade, the country is still unlikely to meet the Millennium Development Goal #4. Of significance, there has been minimal improvement in the early neonatal mortality rate, which is an indicator of quality of perinatal care. In the efforts to address this aspect, a range of efforts and interventions have been considered. One such effort is in addressing and reducing hypothermia in neonates. Two low tech strategies, professional mummying/swaddling (PM/S) and ‘Kangaroo mother care ’ (KMC), are seen as critical in the continuum of neonatal care. Objective: This study compared the effects of KMC and professional mummying/swaddling (PM/S) on select
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".