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Record W4403894423 · doi:10.3389/fonc.2024.1477448

Radioresistance and brain metastases: a review of the literature and applied perspective

2024· review· en· W4403894423 on OpenAlexaff
Andrew Youssef, Arjun Sahgal, Sunit Das

Bibliographic record

VenueFrontiers in Oncology · 2024
Typereview
Languageen
FieldMedicine
TopicBrain Metastases and Treatment
Canadian institutionsSt. Michael's HospitalSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsRadioresistancePerspective (graphical)MedicineMedical physicsGlioblastomaRadiation therapyOncologyInternal medicineCancer researchComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Intracranial metastatic disease is a serious complication of cancer, treated through surgery, radiation, and targeted therapies. The central role of radiation therapy makes understanding the radioresistance of metastases a priori a key interest for prognostication and therapeutic development. Although historically defined clinic-radiographically according to tumour response, developments in new techniques for delivering radiation treatment and understanding of radioprotective mechanisms led to a need to revisit the definition of radioresistance in the modern era. Factors influencing radioresistance include tumour-related factors (hypoxia, cancer stem cells, tumour kinetics, tumour microenvironment, metabolic alterations, tumour heterogeneity DNA damage repair, non-coding RNA, exosomes, methylomes, and autophagy), host-related factors (volume effect & dose-limiting non-cancerous tissue, pathophysiology, and exosomes), technical factors, and probabilistic factors (cell cycle and random gravity of DNA damage). Influences on radioresistance are introduced and discussed in the context of brain metastases.

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 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 score0.750
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.354
Teacher spread0.337 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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