Optimized control of gas temperature and relative humidity during non-invasive respiratory support in preterm infants: A simple and low-cost intervention
Bibliographic record
Abstract
Background: During non-invasive respiratory support, administration of gases at high flow rates requires proper air conditioning to avoid upper airways complications. However, closed-loop control of temperature & relative humidity (T&RH) of mixed gases systems with wired-heated circuits are not commonly available in low-middle income countries (LMIC). Thus, a policy for close control of gas T&RH to overcome our system limitations was implemented. The objective of this study was to evaluate the impact of this intervention. Method: Single center retrospective study including preterm infants admitted to a level II unit (June 2019–2020). Close control of gas T&RH was implemented in Dec 2019 and consisted of starting with the heater at a set number higher than previously and followed by adjustments every 6 h based on intermittent temperature checks (target: 36.5°C–37°C). Humidity was controlled by adjusting the water level every 6 h and checking for the presence of condensation. Two groups were identified: infants treated with or without the close T&RH control. Primary outcome was the occurrence of nasal bleeding. Secondary outcomes were adherence to the policy, need to change the original mode of respiratory support or endotracheal intubation. Appropriate statistical tests were applied. Results: A total of 128 neonates were included: 63 managed without and 65 with close gas T&RH control. Rates of nasal bleeding decreased significantly with the close T&RH control, from 30% to 14% ( p = 0.026) despite longer duration of non-invasive support (CPAP: 1.8 ± 1.9 vs 4.3 ± 5.8 days; p = 0.002 and NIPPV: 1.8 ± 1.4 vs 4.2 ± 5.2; p = 0.009). Adherence to the policy was 93%. Changes of the original mode of respiratory support also decreased significantly (14% vs 3%; 0.024) with no intubation. Conclusion: In the absence of advanced and expensive systems, a simple and low-cost intervention to optimize gas T&RH during non-invasive respiratory support in LMIC is feasible and associated with clinical improvements.
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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 | 0.002 |
| 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 | 0.001 |
| 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".