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Record W2102417019 · doi:10.1002/mrm.21644

Visualizing water clearance in the lung with MRI

2008· article· en· W2102417019 on OpenAlexafffund
Logi Vidarsson, Emma Helm, Hugh OʼBrodovich, Christopher K. Macgowan

Bibliographic record

VenueMagnetic Resonance in Medicine · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersHeart and Stroke Foundation of Canada
KeywordsLungGadoliniumClearance rateNuclear medicineTRACERMedicineChemistryInternal medicinePhysics

Abstract

fetched live from OpenAlex

Current indirect measurements of alveolar fluid clearance (AFC) suggest that the rate of fluid clearance correlates with morbidity and mortality in patients with pulmonary edema. In a traditional AFC-measurement, fluid laced with a tracer macromolecule is instilled into the lung and thereafter repeated samples of the instilled fluid are extracted from the lung's fluid-filled airspaces. The change in concentration of the tracer molecule indicates the AFC-rate. In this work, a new MRI technique was developed to image lung water clearance by adding Gadolinium-DTPA to the instilled fluid. As fluid is absorbed by the animal, the concentration of gadolinium will increase, reducing the T(1) relaxation time. By repeatedly measuring the T(1) relaxation time, the AFC can be tracked over time with high spatial resolution. The new technique was tested both in phantoms and 10 Yorkshire piglets.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.446

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 head0.018
GPT teacher head0.295
Teacher spread0.276 · 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 designObservational
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

Citations3
Published2008
Admission routes2
Has abstractyes

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