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Record W2022527223 · doi:10.1089/jamp.2011.0897

Using MRI to Measure Aerosol Deposition

2012· review· en· W2022527223 on OpenAlexaff
Richard B. Thompson, Warren H. Finlay

Bibliographic record

VenueJournal of Aerosol Medicine and Pulmonary Drug Delivery · 2012
Typereview
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAerosolDeposition (geology)Magnetic resonance imagingHuman lungEnvironmental scienceBiomedical engineeringChemistryLungMedicineRadiologyGeologyInternal medicine

Abstract

fetched live from OpenAlex

This article provides a concise review of the use of magnetic resonance imaging (MRI) for measurement of regional aerosol deposition in the lungs. Basic aspects of MRI and its use in lung imaging and measurement of regional ventilation are introduced. Imaging of hydrogen protons (water) and inhaled hyperpolarized gases as the MRI source signals are discussed. The addition of contrast agents to aerosol particles in order to allow measurement of regional aerosol deposition is considered. Existing in vitro human and in vivo animal model measurements of regional aerosol deposition in the respiratory tract demonstrate the capability of MRI in this regard. However, as a tool for human deposition studies, current approaches require contrast agent doses that are too high to be considered competitive with traditional radionuclide aerosol deposition measurement methods. Thus, future use of MRI in human studies of regional aerosol deposition is predicated on improvement over present approaches.

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)0.001
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 score0.003
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.090
GPT teacher head0.349
Teacher spread0.259 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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