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Record W2898952978 · doi:10.3747/co.25.4002

Radiation Therapy and Immunotherapy—A Potential Combination in Cancer Treatment

2018· review· en· W2898952978 on OpenAlexvenueno aff
Noam Asna, Alejandro Livoff, Ron Batash, Ronen Debbi, P.M. Schaffer, T. Rivkind, Moshe Schaffer

Bibliographic record

VenueCurrent Oncology · 2018
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImmunotherapyRadiation therapyCancerCancer treatmentCancer immunotherapyBioinformaticsCancer researchInternal medicineBiology

Abstract

fetched live from OpenAlex

Background: Radiation therapy (rt) is a longstanding treatment modality for cancer. In addition, immune checkpoint blockade has been a significant development in the field of immunotherapy, modifying key immunosuppressive pathways of cancer cells. Methods: The aim of the present work was to review current concepts of rt and immunotherapy synergism, the abscopal effect, and the molecular effects of rt in the tumour microenvironment, its influence on immune stimulation, and potential clinical outcomes that might evolve from ongoing studies. We also discuss potential predictors of clinical response. Results: Up-to-date literature concerning the mechanisms, interactions, and latest knowledge about rt and immunotherapy was reviewed and summarized, and is presented here. Conclusions: The possibility of using hyperfractionated rt to combine an abscopal effect with the enhanced effect of immune treatment using checkpoint blockade is a very promising method for future tumour treatments.

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 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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.161
GPT teacher head0.463
Teacher spread0.302 · 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
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

Citations39
Published2018
Admission routes1
Has abstractyes

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