Exploring Environmental Factors Contributing to Fluid Loss in Diapers Placed in Neonatal Incubators
Bibliographic record
Abstract
PURPOSE: Assessing fluid output for infants in the neonatal intensive care unit is essential to understanding fluid and electrolyte balance. Wet diaper weights are used as standard practice to quantify fluid output; yet, diaper changes are intrusive and physiologically distressing. Less frequent diaper changes may have physiologic benefits but could alter diaper weights following extended intervals. METHODS: This pilot study examined the impact of initial diaper fluid volume, incubator air temperature and humidity, and diaper brand on wet diaper weight over time. Baseline fluid volume was instilled, and then diapers were placed in a neonatal incubator. Wet diaper weight was assessed longitudinally to determine changes in fluid volume over time. A factorial design with repeated measures (baseline, 3 hours, and 6 hours) was used to explore the effects of diaper brand (brand 1 vs brand 2), baseline fluid volume (3 mL vs 5 mL), and incubator temperature (28°C vs 36°C) and humidity (40% vs 80%) on the trajectory of weight in 80 diapers. RESULTS: Wet diaper weight was significantly reduced over 6 hours ( P < .005). However, wet diaper weight increased in 80% humidity, but decreased in the 40% humidity over time ( P < .0001). Baseline fluid volume, incubator temperature, and diaper brand did not influence wet diaper weight over time (all P > .05). IMPLICATIONS: Understanding environmental factors that influence the trajectory of wet diaper weight may support clinicians in optimizing the interval for neonatal diaper changes to balance the impact of intrusive care with need to understand fluid volume loss.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".