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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 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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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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