Incidence and risk factors of neonatal hypothermia: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Hypothermia poses a threat to the health and lives of newborns. Therefore, it is essential to identify the factors that influence neonatal hypothermia and provide targeted intervention suggestions for clinical practice to reduce its occurrence. METHODS: We conducted a literature search to identify factors influencing neonatal hypothermia and performed a meta-analysis to determine the prevalence of neonatal hypothermia and its associated factors. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of cohort and case-control studies, while the Agency for Healthcare Research and Quality (AHRQ) was used to evaluate the quality of cross-sectional studies. RESULTS: Eighteen studies involving 44 532 newborns from 13 countries were included. The incidence of neonatal hypothermia was 52.5% (95% CI: 0.37, 0.68). Factors such as no skin-to-skin contact, prematurity, low birth weight, delayed breastfeeding, asphyxiation and resuscitation after birth, low APGAR score, not wearing a cap, and caesarean section were found to affect neonatal hypothermia. CONCLUSION: Multiple factors influence neonatal hypothermia, and clinicians can utilise these factors to develop targeted intervention measures to prevent and reduce the incidence of neonatal hypothermia.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".