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The IASLC Lung Cancer Staging Project: Analysis of Resection Margin Status and Proposals for Residual Tumor Descriptors for Non–Small Cell Lung Cancer
John Edwards, Kari Chansky, Paul Van Schil, Andrew G. Nicholson, Souheil Boubia, Élisabeth Brambilla, Jessica Donington, Françoise Galateau-Sallé, Hans Hoffmann, Maurizio Infante, Mirella Marino, Edith M. Marom, Jun Nakajima, Marcin Ostrowski, William D. Travis, Ming‐Sound Tsao, Yasushi Yatabe, Dorothy J. Giroux, Lynn Shemanski, John Crowley, M Krasnik, Hisao Asamura, Ramón Rami–Porta, Valerie W. Rusch, Luiz H. Araujo, David G. Beer, Pietro Bertoglio, Andrea Billè, Vanessa Bolejack, James D. Brierley, Ayten Cangır, David P. Carbone, Gail Darling, Frank C. Detterbeck, Xavier Benoît D’Journo, Jessica Donnington, Wilfried Eberhardt, Jeremy Erasmus, Conrad Falkson, Wentao Fang, Dean A. Fennell, Kwun M. Fong, Oliver Gautschi, Ritu R. Gill, Meredith Giuliani, Jin Mo Goo, Seiki Hasegawa, Fred R. Hirsch, Hans Hoffman, Wayne L. Hofstetter, James Huang, Philippe Joubert, Kemp H. Kernstine, Keith M. Kerr, Young Tae Kim, Hong Kwan Kim, Hedy L. Kindler, Yolande Lievens, Hui Liu, Donald E. Low, Gustavo Lyons, Heber MacMahon, José-María Matilla, Jan P. van Meerbeeck, Luis M. Montuenga, Anna K. Nowak, Isabelle Opitz, Meinoshin Okumura, Raymond U. Osarogiagbon, Harvey I. Pass, Marc de Perrot, Helmut Prosch, David C. Rice, Andreas Rimner, Enrico Ruffini, Shuji Sakai, Navneet Singh, Amy Stoll-D’Astice, Francisco Su ́rez, Ricardo Mingarini Terra, Paula A. Ugalde, David Waller, Shun‐ichi Watanabe, Jacinta Wiens, Ignacio I. Wistuba, Liyan Jiang, Kaoru Kubota, Akif Turna, Benny Weksler, Maria Teresa Tzukazan, Martin C. Tammemägi, Charles A. Powell, David P. Naidich, Hongxu Liu, Samuel G. Armato, Giuseppe Cardillo, Elizabeth A. David, Brigitte Fournier, Mark Krasnik, K. Kubota, Catherine Labbé, Eric Lim, Paul Martin Putora, Gaetano Rocco, Pier Luigi Filosso, Kazuya Kondo, Giuseppe Giaccone, Marco Lucchi, Thomas W. Rice, Mark Ferguson, Prasad Adsusmilli, Francisco Suárez, Kaura Kubota, Watanabe Hisao Asamura Shun-ichi, Rami-Porta Edith Marom Ramón, Watanabe Shun'ichi, Meredith Guiliani, Ray U. Osarogiagbon, Hongwei Wang, F Galateau, Jim Mo Goo, Bill Travis, José María Matilla, Carolle St. Pierre, Ma Teresa Tzukazan, N. Girard, Andreas Rimmer, Prasad S. Adusumilli, Adam Rosenthal
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.
Bibliographic record
VenueJournal of Thoracic Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineLung cancerLymph nodeDissection (medical)Resection marginCarcinomaSurgeryRadiologyLungResectionOncologyInternal medicine
Abstract
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.
Full frame distilled prediction
Teacher imitationNot 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 score0.383
Threshold uncertainty score0.432
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.023
GPT teacher head0.419
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itQuick stats
Citations157
Published2019
Admission routes1
Has abstractno