Reducing hypothermia in preterm babies by usage of bubble wrap
Bibliographic record
Abstract
Objective: To determine if using bubble wrap in the routine care of preterm babies will reduce the incidence of hypothermia after transfer from the Neonatal Intensive Care Unit (NICU) to the High-Dependency Unit (HDU). Materials and Methods: This is an interventional study conducted in a tertiary-level NICU (Bangalore, India) from April 2018 to September 2023. The study included 140 preterm babies with different gestational ages and birth weights who were shifted or admitted to the HDU during that period. The babies’ vital parameters (heart rate, temperature, oxygen saturation, and respiratory rate) were monitored on a four-hourly basis for 2 days before and 2 days after intervention, and the results were recorded. Descriptive statistics were used for statistical analysis. Results: The incidence of hypothermia before using bubble wrap was 44.3 %, while no cases of hypothermia were observed after its application. The mean temperature of the infants before using bubble wrap was 36.66 ± 0.06 °C, compared to 36.72 ± 0.06 °C afterward, with a p-value of 0.0001, indicating statistical significance. Conclusion: Bubble wrap is a safe and cost-effective measure in preventing hypothermia in preterm infants.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".