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Record W2568438117 · doi:10.1016/j.jtho.2016.11.1587

P3.01-021 Reproducibility of Comprehensive Histologic Assessment and Refining Histologic Criteria in P Staging of Multiple Tumor Nodules

2017· article· en· W2568438117 on OpenAlexaff
Andrew G. Nicholson, Patrizia Viola, Kathleen C. Torkko, Edwina Duhig, Kim R. Geisinger, Alain Borczuk, Kenzo Hiroshima, Ming‐Sound Tsao, Arne Warth, Sylvie Lantuéjoul, Prudence A. Russell, Erik Thunnissen, Alberto M. Marchevsky, Mari Mino–Kenudson, Mary Beth Beasley, Johan Botling, Sanja Đačić, Yasushi Yatabe, Masayuki Noguchi, William D. Travis, Keith M. Kerr, Fred R. Hirsch, Lucian R. Chirieac, Ignacio Wistuba, André L. Moreira, Jin-Haeng Chung, Teh Ying Chou, Lukas Bubendorf, Gang Chen, Giuseppe Pelosi, Claudia Poleri, Wilbur A. Franklin

Bibliographic record

VenueJournal of Thoracic Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicinePathologyPleomorphism (cytology)ConcordanceGrading (engineering)CytopathologyKappaAdenocarcinomaCancerCytologyInternal medicineImmunohistochemistry

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)0.022
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.085
GPT teacher head0.481
Teacher spread0.396 · 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.

Study designObservational
DomainReproducibility
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

Citations1
Published2017
Admission routes1
Has abstractno

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