Bibliographic record
Abstract
The last decade has witnessed momentous changes in the Neonatal health scenario in India. Newborn Health care has received unprecedented attention and resources. It is the current focus of Central and state governments and the various funding agencies. And, like never before, there is an opportunity for the champions of newborn health to take their agenda forward . India contributes to one-fifth of global live births and more than a quarter of neonatal deaths. About two-thirds of infant deaths and half of under-five child deaths are during the neonatal period. The neonatal mortality rate has reduced over the years but the decline is at a much slower pace as compared to deaths in the older infant groups. Among neonatal deaths, the rate of decline in early NMR is much lower than that of late NMR. The Millennium Development Goal-4 (MDG-4), which stipulates a two-thirds reduction in under-five deaths by 2015, obviously cannot be achieved without ensuring a substantial reduction in the neonatal mortality rate (NMR). This article reviews the current status of Neonatal Care in India.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".