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Record W2324542191 · doi:10.15326/jcopdf.1.2.2014.0136

Longitudinal Computed Tomography and Magnetic Resonance Imaging of COPD: Thoracic Imaging Network of Canada (TINCan) Study Objectives

2014· article· en· W2324542191 on OpenAlexafffundabout
Miranda Kirby, Damien Pike, David G. McCormack, Don D. Sin, Stephen Lam, Harvey O. Coxson, Grace Párraga

Bibliographic record

VenueChronic Obstructive Pulmonary Diseases Journal of the COPD Foundation · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsBC Cancer AgencyWestern UniversitySt. Paul's HospitalUniversity of British Columbia
FundersCanadian Institutes of Health ResearchReseau canadien de recherche respiratoire
KeywordsCOPDAcknowledgementMagnetic resonance imagingMedicineComputed tomographyMedical diagnosisRadiologyMedical physicsInternal medicineComputer science

Abstract

fetched live from OpenAlex

Xe) MRI. TINCan provides a unique opportunity to quantify and compare novel lung structure-function measurements and investigate their relationship with well-established clinical measurements and outcomes. Such intermediate endpoints of COPD may be used to stratify patients for personalized treatments and to develop new treatments to improve outcomes, a long-standing clinical goal.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.248
Teacher spread0.242 · 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 designObservational
Domainnot available
GenreProtocol

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
Published2014
Admission routes3
Has abstractyes

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