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Record W4400689898 · doi:10.1148/radiol.233265

CT Chest Imaging Using Normalized Join-Count: Predicting Emphysema Progression in the CanCOLD Study

2024· article· en· W4400689898 on OpenAlexaff
S. Virdee, Wan C. Tan, James C. Hogg, Jean Bourbeau, Cameron Hague, Miranda Kirby, Jonathon Samet, Milo A. Puhan, Qutayba Hamid, Carolyn J. Baglole, Palmina Mancino, Peizhi Li, Zhi Gang Song, Dennis Jensen, Benjamin M. Smith, Yvan Fortier, Mina Dligui, Kenneth R. Chapman, Jane Duke, Andrea S. Gershon, J. Mark FitzGerald, Mohsen Sadatsafavi, Christine Lo, Sarah Cheng, Elena Un, Cynthia Fung, Nancy Haynes, Liyun Zheng, LingXiang Zou, Joe Comeau, Brandie Walker, Curtis Dumonceaux, Paul Hernandez, Scott Fulton, Shawn D. Aaron, Kathy Vandemheen, Denis O’Donnell, Matthew McNeil, Kate Whelan, François Maltais, Cynthia Brouillard, Darcy D. Marciniuk, Ron Clemens, Janet Baran

Bibliographic record

VenueRadiology · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsUniversity of SaskatchewanUniversité LavalUniversity of OttawaDalhousie UniversityUniversity of TorontoMcGill UniversityUniversity of CalgaryQueen's UniversityUniversité de SherbrookeUniversity of British Columbia
Fundersnot available
KeywordsMedicinePulmonary emphysemaJoin (topology)RadiologyNuclear medicineInternal medicineLungCombinatorics

Abstract

fetched live from OpenAlex

Using CT normalized join-count to quantify emphysema voxel compactness, this study demonstrates its predictive capability for emphysema progression as quantified by CT lung density and decline in lung function and gas transfer in individuals with chronic obstructive pulmonary disease.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.361
Teacher spread0.334 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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