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Record W4389454770 · doi:10.1016/j.jtho.2023.12.006

The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for the Revisions of the T-Descriptors in the Forthcoming Ninth Edition of the TNM Classification for Lung Cancer

2023· article· en· W4389454770 on OpenAlexfundno aff
Paul Van Schil, Hisao Asamura, Katherine K. Nishimura, Ramón Rami–Porta, Young Tae Kim, Pietro Bertoglio, Ayten Cangır, Jessica Donington, Wentao Fang, Dorothy J. Giroux, Yolande Lievens, Hui Liu, Gustavo Lyons, Shuji Sakai, William D. Travis, Paula A. Ugalde, Chi‐Fu Jeffrey Yang, Masaya Yotsukura, Frank C. Detterbeck

Bibliographic record

VenueJournal of Thoracic Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersNational Cancer InstituteInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalArthrex GmbHSamsungNational Institutes of HealthLilly DeutschlandShanghai Chest HospitalAIO-StudienTokyo Women's Medical UniversityPeking UniversityUniversità degli Studi di TorinoMedizinische Universität WienUniversitätsspital ZürichUniversidade de São PauloHyogo College of MedicineShanghai Jiao Tong UniversitySichuan UniversityUniversiteit GentSeoul National University HospitalGazi ÜniversitesiUniversität WienIstanbul Üniversitesi-CerrahpasaAstraZenecaInternational Association for the Study of Lung CancerUniversity of AberdeenMount Sinai Health SystemUniversity of TorontoImperial College LondonBristol-Myers SquibbUniversité LavalUniversitair Ziekenhuis AntwerpenTechnische Universität MünchenAnkara UniversitesiAix-Marseille UniversitéYork UniversityCleveland ClinicUniversidad de NavarraPostgraduate Institute of Medical Education and Research, ChandigarhUniversity of LeicesterUniversity of ChicagoUniversitair Ziekenhuis GentNYU Langone Medical CenterBoehringer IngelheimOhio State UniversityMassachusetts General HospitalQueen's UniversityUniversity of UlsanUniversity of Texas Southwestern Medical CenterSeoul National UniversityYale UniversityMemorial Sloan-Kettering Cancer CenterEli Lilly and CompanyCelgene
KeywordsMedicineLung cancerNinthUnivariate analysisProportional hazards modelCancerInternal medicineLungOncologyRadiologyMultivariate analysis

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)0.004
metaresearch head score (Gemma)0.001
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 score0.200
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.061
GPT teacher head0.492
Teacher spread0.431 · 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

Citations67
Published2023
Admission routes1
Has abstractno

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