MétaCan
Menu
Back to cohort
Record W2892837443 · doi:10.1016/j.jtho.2018.08.2036

Current and Future Management of Malignant Mesothelioma: A Consensus Report from the National Cancer Institute Thoracic Malignancy Steering Committee, International Association for the Study of Lung Cancer, and Mesothelioma Applied Research Foundation

2018· article· en· W2892837443 on OpenAlexaff
Anne S. Tsao, O. Wolf Lindwasser, Alex A. Adjei, Prasad S. Adusumilli, Matthew Beyers, Gideon M. Blumenthal, Raphael Bueno, Bryan M. Burt, Michele Carbone, Suzanne E. Dahlberg, Marc de Perrot, Dean A. Fennell, Joseph S. Friedberg, Ritu R. Gill, Daniel R. Gomez, David H. Harpole, Raffit Hassan, Mary Hesdorffer, Fred R. Hirsch, Julija Hmeljak, Hedy L. Kindler, Edward L. Korn, Geoffrey Liu, Aaron S. Mansfield, Anna K. Nowak, Harvey I. Pass, Tobias Peikert, Andreas Rimner, B. W. Robinson, Kenneth E. Rosenzweig, Valerie W. Rusch, Ravi Salgia, Boris Sepesi, Charles B. Simone, Rajeshwari Sridhara, Peter W. Szlosarek, Emanuela Taioli, Ming‐Sound Tsao, Haining Yang, Marjorie G. Zauderer, Shakun Malik

Bibliographic record

VenueJournal of Thoracic Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsUniversity of TorontoToronto General HospitalPrincess Margaret Cancer CentreUniversity Health Network
FundersNational Cancer InstituteMesothelioma Applied Research Foundation
KeywordsMedicineMesotheliomaMalignancyLung cancerCancerAsbestosFoundation (evidence)OncologyIntensive care medicinePathologyInternal medicine

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)0.051
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.271

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0060.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0010.001

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 head0.062
GPT teacher head0.475
Teacher spread0.414 · 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 designNot applicable
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

Citations115
Published2018
Admission routes1
Has abstractno

Explore more

Same venueJournal of Thoracic OncologySame topicOccupational and environmental lung diseasesFrench-language works237,207