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Record W3029508145 · doi:10.1016/j.jacr.2020.04.024

Management of Lung Nodules and Lung Cancer Screening During the COVID-19 Pandemic

2020· article· en· W3029508145 on OpenAlexaff
Peter J. Mazzone, Michael K. Gould, Douglas A. Arenberg, Alexander C. Chen, Humberto Choi, Frank C. Detterbeck, Farhood Farjah, Kwun M. Fong, Jonathan M. Iaccarino, Sam M. Janes, Jeffrey P. Kanne, Ella A. Kazerooni, Heber MacMahon, David P. Naidich, Charles A. Powell, Suhail Raoof, M. Patricia Rivera, Nichole T. Tanner, Lynn Tanoue, Alain Tremblay, Anil Vachani, Charles S. White, Renda Soylemez Wiener, Gerard A. Silvestri

Bibliographic record

VenueJournal of the American College of Radiology · 2020
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineLung cancerGuidelineLung cancer screeningPulmonologistsNodule (geology)LungPandemicIntensive care medicineNational Lung Screening TrialFamily medicineRadiologyDiseaseCoronavirus disease 2019 (COVID-19)Internal medicinePathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.317
Teacher spread0.296 · 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

Citations48
Published2020
Admission routes1
Has abstractno

Explore more

Same venueJournal of the American College of RadiologySame topicLung Cancer Diagnosis and TreatmentFrench-language works237,207