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Record W2332310020 · doi:10.1248/yakushi.14-00235-1

Radiation Therapy and Redox Imaging

2015· review· en· W2332310020 on OpenAlexaff
Ken‐ichiro Matsumoto

Bibliographic record

VenueYAKUGAKU ZASSHI · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsRedoxRadicalReactive oxygen speciesChain reactionRadiation therapyMagnetic resonance imagingChemistryIonizing radiationCancer therapyOxygenPhotochemistryMedicineCancerIrradiationBiochemistryRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Radiation therapy kills cancer cells in part by flood of free radicals. Radiation ionizes and/or excites water molecules to create highly reactive species, i.e. free radicals and/or reactive oxygen species. Free radical chain reactions oxidize biologically important molecules and thereby disrupt their function. Tissue oxygen and/or redox status, which can influence the course of the free radical chain reaction, can affect the efficacy of radiation therapy. Prior observation of tissue oxygen and/or redox status is helpful for planning a safe and efficient course of radiation therapy. Magnetic resonance-based redox imaging techniques, which can estimate tissue redox status non-invasively, have been developed not only for diagnostic information but also for estimating the efficacy of treatment. Redox imaging is now spotlighted to achieve radiation theranostics.

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)0.000
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 score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.343
Teacher spread0.311 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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