Short-Term Effects of Humidification Devices on Respiratory Pattern and Arterial Blood Gases During Noninvasive Ventilation
Bibliographic record
Abstract
BACKGROUND: The impact of humidification devices on ventilatory and arterial blood gases parameters during noninvasive ventilation (NIV) remains controversial. The aim of the study was to compare the short-term impact of heat and moisture exchangers (HMEs) and heated humidifiers (HHs) during NIV for either hypercapnic or hypoxemic acute respiratory failure. METHODS: Consecutive subjects receiving NIV were successively treated with HME and HH in randomized order for 30 min each. At the end of each period, arterial blood gases were measured and ventilatory parameters were recorded. RESULTS: Eighty-one subjects were enrolled, of whom 52 were hypercapnic (with or without acidosis) and 29 hypoxemic. Minute ventilation was greater with the HME, in comparison with the HH (15 [12-18] vs 12 [10-16] median [interquartile range], P < .001), while P(aCO(2)) was increased when using HME, indicating a dead space effect. This effect was observed in all subjects, but was more pronounced in hypercapnic subjects (P(aCO(2)) 62 ± 17 mm Hg with HME vs 57 ± 14 with HH, P < .001). In a subgroup of 19 subjects with respiratory acidosis, alveolar hypoventilation improved only with the HH. The amplitude of the dead space impact was a function of the degree of hypercapnia. CONCLUSIONS: Use of an HME decreased CO(2) elimination during NIV, despite increased minute ventilation, especially in hypercapnic subjects.
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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 | 0.000 |
| 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".