Fluctuations in relative humidity provided to extremely low‐birthweight infants (R1)
Bibliographic record
Abstract
BACKGROUND: In extremely low-birthweight infants, the addition of relative humidity (RH) improves thermal stability, fluid and electrolyte balance. However, during routine care this microenvironment is frequently disturbed. The objective of this study was to determine the frequency, magnitude and direction of fluctuations in RH provided to extremely low-birthweight infants. METHODS: All infants in our study had ambient temperature and RH continuously recorded for 48 h using a datalogger device (RH32S-C2). A clinically acceptable range for RH was defined as the set point ± 10%. A secondary analysis was performed to compare outcomes between infants that spent > 50% of the time out-of-range (OOR) or inside the range (IR). A P-value < 0.05 was significant. RESULTS: A total of 20 infants were included. Important fluctuations were detected by the device with infants spending 40% and 14% of the time above and below the range, respectively. However, the RH set point did not differ from the mean levels measured over 48 h by the RH32S-C2 or the incubator. Infants in the OOR group spent significantly more time at values higher than the planned range when compared to IR infants. CONCLUSION: Although significant fluctuations in RH above the desired range were detected in more than half of the infants, the average values were similar to the set points. Nevertheless, knowledge of these dynamic changes may help to optimize individualized care.
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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 | 0.003 |
| 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".