Impact of Humidification and Nebulization During Expiratory Limb Protection: An Experimental Bench Study
Bibliographic record
Abstract
BACKGROUND: Different filtering devices are used during mechanical ventilation to avoid dysfunction of flow and pressure transducers or for airborne microorganisms containment. Water condensates, resulting from the use of humidifiers, but also residual nebulization particles may have a major influence on expiratory limb resistance. OBJECTIVES: To evaluate the influence of nebulization and active humidification on the resistance of expiratory filters. METHODS: A respiratory system analog was constructed using a test lung, an ICU ventilator, heated humidifiers, and a piezoelectric nebulizer. Humidifiers were connected to different types of circuits (unheated, mono-heated, new-generation and old-generation bi-heated). Five filter types were evaluated: electrostatic, heat-and-moisture exchanger, standard, specific, and internal heated high-efficiency particulate air [HEPA] filter. Baseline characteristics were obtained from each dry filter. Differential pressure measurements were carried out after 24 hours of continuous in vitro use for each condition, and after 24 hours of use with an old-generation bi-heated circuit without nebulization. RESULTS: While using unheated circuits, measurements had to be interrupted before 24 hours for all the filtering devices except the internal heated HEPA filter. The heat-and-moisture exchangers occluded before 24 hours with the unheated and mono-heated circuits. The circuit type, nebulization practice, and duration of use did not influence the internal heated HEPA filter resistance. CONCLUSION: Expiratory limb filtration is likely to induce several major adverse events. Expiratory filter resistance increase is due mainly to the humidification circuit type, rather than to nebulization. If filtration is mandatory while using an unheated circuit, a dedicated filter should be used for ≤ 24 hours, or a heated HEPA for a longer duration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".