Improving clinical outcomes of very low birth weight infants
Bibliographic record
Abstract
BACKGROUND: Standardized written guidelines and protocols in NICU are known to impact neonatal outcomes and improve survival. OBJECTIVE: To study and compare the morbidity and mortality outcomes of very low birth weight (VLBW) neonates before and after introduction of structured approach to standardized management guidelines on four interventions in a tertiary care hospital in North India. METHODOLOGY: Structured approach to standardized management guidelines on four interventions were implemented for VLBW infants in NICU. a) Humidified and Heated High Flow Nasal Cannula (HHHFNC) as the initial mode of ventilator support in preterm VLBW babies. b) Expressed breast milk for feeding preterm VLBW babies and absolutely no formula milk. c) Hand washing and following "Bundle Care Approach" for Central lines as the cardinal cornerstones for maintaining strict asepsis. d) Development and supportive care to be regularly followed. Data was collected prospectively from July 2015 to December 2016 (Intervention Group) and compared with retrospective matched controls from the previous year (July 2014-June 2015) (Control Group). RESULTS: = .74) amongst the two groups. CONCLUSION: Implementing structured approach to above mentioned interventions in the form of standardized management guidelines for preterm VLBW neonates was associated with significant reduction in culture proven sepsis and mechanical ventilation days without affecting mortality or other co-morbidities.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".