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Record W3042859035 · doi:10.1016/j.jgo.2020.07.008

Adapting care for older cancer patients during the COVID-19 pandemic: Recommendations from the International Society of Geriatric Oncology (SIOG) COVID-19 Working Group

2020· article· en· W3042859035 on OpenAlexaff
Nicolò Matteo Luca Battisti, Anna Rachelle Mislang, Lisa Cooper, Anita O’Donovan, Riccardo A. Audisio, Kwok‐Leung Cheung, Regina Gironés Sarrió, Reinhard Stauder, Enrique Soto‐Pérez‐de‐Celis, Michael T. Jaklitsch, Grant R. Williams, Shane O’Hanlon, Mahmood Alam, Clarito Cairo, Giuseppe Colloca, Luiz Antonio Gil, Schroder Sattar, Kumud Kantilal, Chiara Russo, Stuart M. Lichtman, Étienne Brain, Ravindran Kanesvaran, Hans Wildiers

Bibliographic record

VenueJournal of Geriatric Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of Saskatchewan
FundersNational Cancer InstituteInstitute of Cancer ResearchNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchNational Institute for Health and Care ResearchNIHR Bristol Biomedical Research CentrePharmacy Research UKRoyal Marsden NHS Foundation Trust
KeywordsMedicinePandemicGeriatric oncologyIntensive care medicineAdverse effectPopulationHealth careCancerCoronavirus disease 2019 (COVID-19)Radiation therapyInternal medicineDiseaseEnvironmental health

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)0.050
metaresearch head score (Gemma)0.127
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: Methods · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.127
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0030.003
Science and technology studies0.0060.004
Scholarly communication0.0110.009
Open science0.0080.025
Research integrity0.0200.026
Insufficient payload (model declined to judge)0.0080.004

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 head0.131
GPT teacher head0.437
Teacher spread0.306 · 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
GenreMethods

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

Citations80
Published2020
Admission routes1
Has abstractno

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