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Record W4381850030 · doi:10.1016/j.jgo.2023.101564

SIOG COVID-19 Working Group recommendations on COVID-19 therapeutic approaches in older adults with cancer

2023· article· en· W4381850030 on OpenAlexaff
Chiara Russo, Anna Rachelle Mislang, Domenico Ferraioli, Enrique Soto‐Pérez‐de‐Celis, Giuseppe Colloca, Grant R. Williams, Shane O’Hanlon, Lisa Cooper, Anita O’Donovan, Riccardo A. Audisio, Kwok‐Leung Cheung, Regina Gironés Sarrió, Reinhard Stauder, Michael T. Jaklitsch, Clarito Cairo, Luiz Antonio Gil, Schroder Sattar, Kumud Kantilal, Kah Poh Loh, Stuart M. Lichtman, Étienne Brain, Ravindran Kanesvaran, Nicolò Matteo Luca Battisti

Bibliographic record

VenueJournal of Geriatric Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of SaskatchewanTrinity College
FundersNational Cancer InstituteSanofiNovartis
KeywordsMedicineScopusImmunogenicityImmunosenescenceCoronavirus disease 2019 (COVID-19)CancerVaccinationMEDLINEInternal medicineImmune systemOncologyImmunologyDiseaseInfectious disease (medical specialty)

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)0.057
metaresearch head score (Gemma)0.141
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0570.141
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0060.003
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0050.011
Research integrity0.0130.012
Insufficient payload (model declined to judge)0.0160.009

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 head0.189
GPT teacher head0.449
Teacher spread0.260 · 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
GenreEditorial

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
Published2023
Admission routes1
Has abstractno

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