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Record W4383647203 · doi:10.1177/17588359231183668

Radiation and immune checkpoint inhibitors in the treatment of oligometastatic non-small-cell lung cancer: a practical review of rationale, recent data, and research questions

2023· review· en· W4383647203 on OpenAlexaff
Sondos Zayed, Alexander V. Louie, Daniel Breadner, David A. Palma, Rohann Correa

Bibliographic record

VenueTherapeutic Advances in Medical Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsSunnybrook HospitalLondon Health Sciences Centre
Fundersnot available
KeywordsSABR volatility modelMedicineLung cancerOncologyClinical trialRadiation therapyNarrative reviewInternal medicineMedical physicsIntensive care medicine

Abstract

fetched live from OpenAlex

The combined use of stereotactic ablative radiotherapy (SABR) and immune checkpoint inhibitors (ICIs) is an emerging treatment paradigm for oligometastatic non-small-cell lung cancer (NSCLC). Recent phase I and II trial data suggest that SABR to multiple metastases in addition to ICI use is safe and effective with promising progression-free survival and overall survival signals. There is great interest in capitalizing on combined immunomodulation from these two modalities for the treatment of oligometastatic NSCLC. Ongoing trials seek to validate the safety, efficacy, and preferred sequencing of SABR and ICI. This narrative review of the role of SABR when combined with ICI in oligometastatic NSCLC discusses the rationale for this bimodality treatment, summarizes recent clinical trial evidence, and proposes key principles of management based on the available evidence.

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)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
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 head0.147
GPT teacher head0.524
Teacher spread0.377 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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