Improving Admission Temperature in Infants ≥34 Weeks’ Gestation: A Quality Improvement Initiative
Bibliographic record
Abstract
BACKGROUND: NICU admission for hypothermia is a problem worldwide, with associated morbidity, mortality, and financial costs. Many interventions have been studied for smaller infants, but there has been little focus on infants born ≥34 weeks' gestational age (GA), though most deliveries occur at this gestation. Our primary aim was to improve the proportion of infants ≥34 weeks' GA with normal NICU admission temperature. Secondary outcomes included improvement of the proportion of normal first temperature in all infants ≥34 weeks' GA, independent of NICU admission, and improvement of predefined outcome measures. METHODS: We completed a root cause analysis, using fishbone and process mapping to determine what factors were contributing to hypothermia. A series of changes were trialed using plan-do-study-act cycles to develop a standard operating procedure, covering both vaginal and cesarean section births. Outcome measures were analyzed using a P-chart as well as traditional statistical tests. RESULTS: We successfully increased the proportion of infants ≥34 weeks' GA with normothermia on NICU admission from 62% to 80% without increasing hyperthermia. In addition, the interventions improved the proportion of delivery room normothermia in all infants born ≥34 weeks' GA and were associated with a decreased need for intravenous therapy for hypoglycemia and the incidence of metabolic acidosis. CONCLUSIONS: This quality improvement initiative was successful at improving our institution's rates of normal infant temperature. The methodology used can be applied to other similar centers to improve this common problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".