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Record W7135078917 · doi:10.4187/respcare.20244137581

Comparison Between Various Jet Nebulizers Designed to Provide a Shorter Aerosol Treatment Time

2024· article· en· W7135078917 on OpenAlexaff
Daniel F Fisher, Cathy Doyle, Rubina Ali

Bibliographic record

VenueRespiratory Care · 2024
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsTrudell Medical International (Canada)
Fundersnot available
KeywordsNebulizerMouthpieceAerosolJet (fluid)AerosolizationFilter (signal processing)

Abstract

fetched live from OpenAlex

Background: Providing aerosol therapy in a shorter time span can provide more rapid patient relief and improve workflow in a busy respiratory care department. Breath-enhanced (BE) nebulizers improve the flow to the patient and deliver more medication to the distal airway. The purpose of this study is to determine which nebulizer provides the optimal combination of both short treatment time and ability to provide the greatest amount of aerosol within the respirable range. Methods: Three different nebulizers were evaluated (no. = 5); two breath-enhanced (BE) jet nebulizers (MC300 and TurboMist), and one continuous (CONT) jet nebulizer (Misty Max 10) as control. Each nebulizer was loaded with 3 mL of 2.5 mg albuterol sulfate solution with the gas flow set to deliver 8 L/min. The nebulizer was connected to a spontaneous breathing servo lung model (ASL 5000) with the following settings: VT 500 mL, frequency 10, and I:E ratio of 1:2. A filter was placed between the mouthpiece of the nebulizer and the test lung to capture inspired aerosol. Gas flow was stopped every minute and the filter exchanged for a fresh one. Each nebulizer was run for 1 min after sputter was observed. Nebulizer output was determined by rinsing the filter medium in methanol and then placing the filtrate into HPLC. Parallel measurements of fine droplet fraction < 4.7 μm diameter (FDF < 4.7 μm) were made using a Spraytec laser diffractometer. Fine droplet mass (FDM < 4.7 μm) was determined as the product of recovered mass and FDF < 4.7 μm. Results: The TurboMist had the shortest time to sputter with the Misty Max 10 the longest (161.4 ± 6.6 to 287.0 ± 8.7 s, P < .01). Total output mass for each nebulizer was similar for all 3 nebulizers (403.2 ± 42.9 MC300, 339.8 ± 26.3 Misty Max 10, and 333.1 ± 19.6 μg TurboMist, P > .05). However, the FDF < 4.7 μm was significantly higher for one BE device (304.0 ± 32.3 MC300, 202.5 ± 11.9 TurboMist, and 262.5 ± 20.3 μg Misty Max 10, P < .01). Nebulizer efficiency was determined to be FDF < 4.7 μm divided by total output. Overall efficiency of each nebulizer was small (12 ± 1% MC300, 10 ± 1% Misty Max 10, and 8 ± 0% TurboMist, P < .01). Conclusions: All nebulizers tested were fast nebulizers. Device output and medication delivery to the distal airways must be considered when selecting an aerosol generator along with time. Poor aerosol delivery could require more frequent treatments which would negate the benefit of using a higher output nebulizer.Table 1: Fine droplet mass (particles < 4.7 μm ) per device.Figure 1: Median fine droplet mass output by nebulizer

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.335
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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