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Record W2788757939 · doi:10.1089/jamp.2017.1431

Pulmonary Delivery of Therapeutic and Diagnostic Gases

2018· article· en· W2788757939 on OpenAlexaff
Warren M. Zapol, H. Cecil Charles, Andrew R. Martin, Rui Carlos Sá, Binglan Yu, Fumito Ichinose, Neil R. MacIntyre, Joseph Mammarappallil, Richard E. Moon, John Z. Chen, Eric T. Geier, Chantal Darquenne, G. Kim Prisk, Ira Katz

Bibliographic record

VenueJournal of Aerosol Medicine and Pulmonary Drug Delivery · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Alberta
FundersNational Institute of General Medical SciencesNational Heart, Lung, and Blood Institute
KeywordsMedicineNebulizerIntensive care medicineDrug deliveryOxygen deliveryMedical physicsPulmonary medicineSession (web analytics)Computer scienceAnesthesiaNanotechnologyOxygenChemistry

Abstract

fetched live from OpenAlex

The 21st Congress for the International Society for Aerosols in Medicine included, for the first time, a session on Pulmonary Delivery of Therapeutic and Diagnostic Gases. The rationale for such a session within ISAM is that the pulmonary delivery of gaseous drugs in many cases targets the same therapeutic areas as aerosol drug delivery, and is in many scientific and technical aspects similar to aerosol drug delivery. This article serves as a report on the recent ISAM congress session providing a synopsis of each of the presentations. The topics covered are the conception, testing, and development of the use of nitric oxide to treat pulmonary hypertension; the use of realistic adult nasal replicas to evaluate the performance of pulsed oxygen delivery devices; an overview of several diagnostic gas modalities; and the use of inhaled oxygen as a proton magnetic resonance imaging (MRI) contrast agent for imaging temporal changes in the distribution of specific ventilation during recovery from bronchoconstriction. Themes common to these diverse applications of inhaled gases in medicine are discussed, along with future perspectives on development of therapeutic and diagnostic gases.

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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.276
Teacher spread0.256 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations5
Published2018
Admission routes1
Has abstractyes

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