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Record W3140185967 · doi:10.1159/000514451

Immunotherapy and Radiotherapy for Older Cancer Patients during the COVID-19 Era: Proposed Paradigm by the International Geriatric Radiotherapy Group

2021· review· en· W3140185967 on OpenAlexaff
Nam P. Nguyen, Brigitta G. Baumert, Eromosele Oboite, Micaela Motta, Gokula Kumar Appalanaido, Meritxell Arenas, Marta Bonet, Alice Zamagni, T. Vuong, Tiberiu Popescu, U. O. Karlsson, Lurdes Trigo, Arthur Sun Myint, Juliette Thariat, Vincent Vinh‐Hung

Bibliographic record

VenueGerontology · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsRadiation therapyCoronavirus disease 2019 (COVID-19)MedicineImmunotherapyGeriatricsCancerGeriatric oncologyGerontology2019-20 coronavirus outbreakRadiation toleranceOncologyInternal medicinePathologyDiseasePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Older cancer patients with locally advanced or metastatic disease may benefit from chemotherapy alone or combined with radiotherapy. However, chemotherapy is often omitted either because of physician bias or because of its underlying comorbidity, thus compromising their survival. The coronavirus disease 19 (COVID-19) pandemic is compounding this issue because of the fear of immunosuppression induced by chemotherapy on the elderly which makes them more vulnerable to the virus. SUMMARY: Immunotherapy has less effect on the patient bone marrow compared to chemotherapy. The potential synergy between radiotherapy and immunotherapy may improve local control and survival for older patients with selected cancer. Preliminary data are encouraging because of better survival and local control in diseases which are traditionally resistant to radiotherapy and chemotherapy such as melanoma and renal cell carcinoma. Key Message: We propose a new paradigm combining immunotherapy at a reduced dose and/or extended dosing intervals and hypofractionated radiotherapy for older patients with selected cancer which needs to be tested in future clinical trials.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.424
Teacher spread0.357 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2021
Admission routes1
Has abstractyes

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