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Record W2008585312 · doi:10.1097/rti.0b013e31829efbe9

Sources of Variation in Quantitative Computed Tomography of the Lung

2013· review· en· W2008585312 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Thoracic Imaging · 2013
Typereview
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsUniversity of British ColumbiaVancouver General HospitalSt. Paul's Hospital
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsMedicineComputed tomographyLungRadiologyLung diseaseTomographyQuantitative computed tomographyLung functionPathologyInternal medicine

Abstract

fetched live from OpenAlex

The goal of quantitative analysis of computed tomography (CT) scans is to understand the anatomic structure that is responsible for the physiological function of the lung. The gold standard for structural analysis requires the examination of tissue, which is not practical in most studies. Quantitative CT allows valuable information on lung structure to be obtained without removal of tissue from the body, thereby aiding longitudinal studies on chronic lung diseases. This review briefly discusses CT analysis of the lung and some of the sources of variation that can cause differences in the CT metrics used for analysis of lung disease. Although there are many sources of variation, this review will show that, if the study is properly designed to take into account these variations and if the CT scanner is properly calibrated, valuable information can be obtained from CT scans that should allow us to study the pathogenesis of lung disease and the effect of treatment.

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)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.036
GPT teacher head0.389
Teacher spread0.353 · 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