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Record W4386607127 · doi:10.1016/j.jtho.2023.07.008

The International Association for the Study of Lung Cancer Thymic Epithelial Tumors Staging Project: An Overview of the Central Database Informing Revision of the Forthcoming (Ninth) Edition of the TNM Classification of Malignant Tumors

2023· review· en· W4386607127 on OpenAlexafffund
Andreas Rimner, Enrico Ruffini, Vanessa Cilento, Emily Goren, Usman Ahmad, Sarit Appel, Andrea Billè, Souheil Boubia, Cecilia Brambilla, Ayten Cangır, Frank Detterbeck, Conrad Falkson, Wentao Fang, Pier Luigi Filosso, Giuseppe Giaccone, Nicolas Girard, Francesco Guerrera, James Huang, Maurizio Infante, Dong Kwan Kim, Marco Lucchi, Mirella Marino, Edith M. Marom, Andrew G. Nicholson, Meinoshin Okumura, Ramón Rami–Porta, Charles B. Simone, Hisao Asamura

Bibliographic record

VenueJournal of Thoracic Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsQueen's University
FundersNational Cancer InstituteYonsei University College of MedicineCollege of Medicine, Seoul National UniversityInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalShanghai Pulmonary HospitalSzegedi TudományegyetemTokyo Women's Medical UniversityShanghai Chest HospitalPeking UniversityUniversità di PisaChina Medical UniversityUniversiteit GentKU LeuvenQueen's UniversityAstellas PharmaSamsungYonsei UniversityEisaiHealth Science Center, University of TennesseeUniversitair Ziekenhuis GentSoochow UniversityUniversiteit AntwerpenSeoul National University HospitalFudan UniversityUniversität WienUniversidade de São PauloHyogo College of MedicineSungkyunkwan UniversityBristol-Myers SquibbSemmelweis EgyetemKanazawa UniversityTechnische Universität MünchenAnkara UniversitesiChugai PharmaceuticalComprehensive Cancer Center, City of HopeAix-Marseille UniversitéUniversity of TorontoImperial College LondonAstraZenecaUniversity of AberdeenInternational Association for the Study of Lung CancerMount Sinai Health SystemUniversity of ChicagoYale UniversityNSW Health PathologyHarvard UniversityMemorial Sloan-Kettering Cancer CenterVarian Medical SystemsSeoul National UniversityAlexion PharmaceuticalsOhio State UniversityKindai UniversityUniversity of Texas Southwestern Medical CenterUniversità degli Studi di PadovaCentro de Investigación Biomédica en Red de CáncerMassachusetts General HospitalMerckUniversity of LeicesterUniversitätsspital ZürichMedizinische Universität WienYork UniversityUniversidad de NavarraUniversité Hassan II de CasablancaPostgraduate Institute of Medical Education and Research, ChandigarhBrigham and Women's HospitalPfizer
KeywordsMedicineNinthLung cancer stagingLung cancerOncologyGeneral surgery

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.010
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.131
GPT teacher head0.494
Teacher spread0.363 · 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
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

Citations36
Published2023
Admission routes2
Has abstractno

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