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Record W4210600867 · doi:10.1016/j.jtho.2021.12.015

A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry

2022· article· en· W4210600867 on OpenAlexaff
Jennifer G. Whisenant, Javier Baena, Alessio Cortellini, Li‐Ching Huang, Giuseppe Lo Russo, Luca Porcu, Selina K. Wong, Christine M. Bestvina, Matthew D. Hellmann, Elisa Roca, Hira Rizvi, Isabelle Monnet, Amel Boudjemaa, Jacobo Rogado, Giulia Pasello, Natasha B. Leighl, Óscar Arrieta, Avinash Aujayeb, Ullas Batra, Ahmed Y. Azzam, Mojca Unk, Mohammed A. Azab, Ardak Zhumagaliyeva, Carlos Gómez-Martín, Juan Bautista Blaquier, Erica J. Geraedts, Giannis Mountzios, Gloria Serrano-Montero, Niels Reinmuth, Linda Coate, Melina E. Marmarelis, Carolyn J. Presley, Fred R. Hirsch, Pilar Garrido, Hina Khan, Alice Baggi, Céline Mascaux, Balázs Halmos, Giovanni Luca Ceresoli, Mary J. Fidler, Vieri Scotti, Anne-Cécile Métivier, L. Falchero, Enriqueta Felip, Carlo Genova, Julien Mazières, Ümit Tapan, Julie R. Brahmer, Emilio Bria, Sonam Puri, Sanjay Popat, Karen L. Reckamp, Floriana Morgillo, Ernest Nadal, Francesca Mazzoni, Francesco Agustoni, Jair Bar, Federica Grosso, Virginie Avrillon, Jyoti D. Patel, Fábio Gomes, Ehab Ibrahim, Annalisa Trama, Anna Bettini, Fabrice Barlési, Anne‐Marie C. Dingemans, Heather A. Wakelee, Solange Peters, Leora Horn, Marina Chiara Garassino, Valter Torri

Bibliographic record

VenueJournal of Thoracic Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsSurgical Specialties (Canada)Princess Margaret Cancer CentreUniversity Health Network
FundersGenentechPuma BiotechnologyItalfarmacoPharmaMarG1 TherapeuticsSanofi GenzymeMerck Sharp and DohmeEisaiSanofiApollomicsInternational Association for the Study of Lung CancerBeiGeneAstraZenecaNovocureRegeneron PharmaceuticalsF. Hoffmann-La RocheEMD SeronoDaiichi Sankyo EuropeNational Cancer InstituteGilead SciencesGlaxoSmithKlineBristol-Myers SquibbEli Lilly and CompanyAmgenPfizerBristol-Myers Squibb Foundation
KeywordsMedicineProcalcitoninCase fatality rateInternal medicineLogistic regressionPneumoniaStage (stratigraphy)CancerDiseaseEpidemiologySepsis

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)0.009
metaresearch head score (Gemma)0.027
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.007
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.053
GPT teacher head0.428
Teacher spread0.375 · 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
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

Citations21
Published2022
Admission routes1
Has abstractno

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