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Record W2765335987 · doi:10.1016/s1470-2045(17)30597-1

Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study

2017· article· en· W2765335987 on OpenAlexafffundabout
Martin C. Tammemägi, Heidi Schmidt, Simon Martel, Annette McWilliams, John R. Goffin, Michael R. Johnston, Garth Nicholas, Alain Tremblay, Geoffrey Liu, Kam Soghrati, Kazuhiro Yasufuku, David Hwang, Francis Laberge, Michel Gingras, Sergio Pasian, Christian Couture, John R. Mayo, Paola V. Nasute Fauerbach, Sukhinder Atkar-Khattra, Stuart Peacock, Sonya Cressman, Diana N. Ionescu, John C. English, Richard J. Finley, John Yee, Serge Puksa, Lori Stewart, Scott Tsai, Ehsan Haider, Colm Boylan, Jean‐Claude Cutz, Daria Manos, Zhaolin Xu, Glenwood Goss, Jean M. Seely, Kayvan Amjadi, Harmanjatinder S. Sekhon, Paul Burrowes, Paul MacEachern, Stefan J. Urbanski, Don D. Sin, Wan C. Tan, Natasha B. Leighl, Frances A. Shepherd, William K. Evans, Ming‐Sound Tsao, Stephen Lam

Bibliographic record

VenueThe Lancet Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsSt. Paul's HospitalQueen's UniversityVancouver General HospitalUniversity of CalgaryOttawa HospitalUniversity Health NetworkDalhousie UniversitySt. Joseph’s Healthcare HamiltonInstitut universitaire de cardiologie et de pneumologie de QuébecJuravinski Cancer CentreMemorial University of NewfoundlandBC Cancer AgencyBrock University
FundersPartenariat Canadien Contre Le CancerTerry Fox Research InstitutePancreatic Cancer Action Network
KeywordsMedicineLung cancerNational Lung Screening TrialLung cancer screeningCancerInternal medicineBody mass indexFamily historyProspective cohort studyIncidence (geometry)Oncology

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 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 score0.142
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.093
GPT teacher head0.396
Teacher spread0.303 · 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

Citations219
Published2017
Admission routes3
Has abstractno

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