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Record W2107552577 · doi:10.4187/respcare.07520283

Electrostatics and Inhaled Medications: Influence on Delivery Via Pressurized Metered-Dose Inhalers and Add-On Devices

2007· article· en· W2107552577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRespiratory Care · 2007
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsTrudell Medical International (Canada)
Fundersnot available
KeywordsMetered-dose inhalerMedicineInhalerElectric chargeInhalationElectrostaticsCharge (physics)AerosolAnesthesiaAsthmaChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The movement of inhaler-generated aerosols is significantly influenced by electrostatic charge on the particles and on adjacent surfaces. Particle charging arises in the aerosol formation process. Since almost all inhalers contain nonconducting components, these surfaces can also acquire charge during manufacture and use. Spacers and valved holding chambers used with pressurized metered-dose inhalers to treat obstructive lung diseases are particularly prone to this behavior, which increases variability in the amount of medication available for inhalation, and this is exacerbated by low ambient humidity. This may result in inconsistent medication delivery. Conditioning the device by washing it with a conductive surfactant (detergent) or using devices made of charge-dissipative/conducting materials can mitigate electrostatic charge. This review discusses sources of electrostatic charge, the processes that influence aerosol behavior, methods to mitigate electrostatic charge, and potential clinical implications.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.303
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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