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Record W1986938146 · doi:10.1089/jam.2006.19.28

Liquid Atomizing: Nebulizing and Other Methods of Producing Aerosols

2006· review· en· W1986938146 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

VenueJournal of Aerosol Medicine · 2006
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNebulizerDrug deliveryDelivery systemNanotechnologyBiomedical engineeringMedicineMaterials scienceAnesthesia

Abstract

fetched live from OpenAlex

Liquid atomization (or nebulization) is the most traditional method of drug delivery to the lung. Although other methods seem to often be preferred for the delivery of new drugs, nebulizers are experiencing a revival, with new devices based on different atomization techniques, and the more traditional jet nebulizers evolving to become "smart nebulizers." These smart devices synchronize delivery with the patient's breath, estimate or measure delivered dose, provide feedback and data storage, and in some cases control breathing maneuvers. Besides adding new features, new nebulizers are also addressing traditional shortcomings, namely, reducing size, bulkiness, and power consumption. But in the longer term, nebulizers are expected to offer even more important features. Following the trend toward individually optimized therapy, nebulizers will be able to estimate deposited dosage and concentrations in the lung. In addition, as progress in nanotechnology allows the development of smart drug carrying particles, advanced liquid nebulization is expected to be the delivery mode of choice for these smart particle aerosols.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.438
Teacher spread0.329 · 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