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Record W7129320410 · doi:10.13112/pc.1001

Reducing hypothermia in preterm babies by usage of bubble wrap

2025· article· W7129320410 on OpenAlexaff
Ranjini G. Naik, Parimala V Thirumalesh

Bibliographic record

VenuePaediatria Croatica · 2025
Typearticle
Language
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsASTER
Fundersnot available
KeywordsHypothermiaNeonatal intensive care unitIncidence (geometry)Gestational ageStatistical analysisIntensive care unit

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.027
GPT teacher head0.349
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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