MétaCan
Menu
Back to cohort
Record W2121595054 · doi:10.1186/1756-9966-31-7

SNPs in DNA repair or oxidative stress genes and late subcutaneous fibrosis in patients following single shot partial breast irradiation

2012· article· en· W2121595054 on OpenAlexaff
Elisabetta Falvo, Lidia Strigari, Gennaro Citro, C. Giordano, Genoveva Ionela Boboc, F Fabretti, Vicente Bruzzaniti, Luca Bellesi, Paola Muti, Giovanni Blandino, Paola Pinnarò

Bibliographic record

VenueJournal of Experimental & Clinical Cancer Research · 2012
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsMcMaster UniversityJuravinski Cancer Centre
Fundersnot available
KeywordsSingle-nucleotide polymorphismXRCC1GSTP1Breast cancerInternal medicineDNA damageXRCC3DNA repairOxidative stressMedicineOncologyFibrosisBiologyBioinformaticsGenotypeCancer researchGeneticsGeneCancerDNA

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to evaluate the potential association between single nucleotide polymorphisms related response to radiotherapy injury, such as genes related to DNA repair or enzymes involved in anti-oxidative activities. The paper aims to identify marker genes able to predict an increased risk of late toxicity studying our group of patients who underwent a Single Shot 3D-CRT PBI (SSPBI) after BCS (breast conserving surgery). METHODS: A total of 57 breast cancer patients who underwent SSPBI were genotyped for SNPs (single nucleotide polymorphisms) in XRCC1, XRCC3, GST and RAD51 by Pyrosequencing technology. Univariate analysis (ORs and 95% CI) was performed to correlate SNPs with the risk of developing ≥ G2 fibrosis or fat necrosis. RESULTS: A higher significant risk of developing ≥ G2 fibrosis or fat necrosis in patients with: polymorphic variant GSTP1 (Ile105Val) (OR = 2.9; 95%CI, 0.88-10.14, p = 0.047). CONCLUSIONS: The presence of some SNPs involved in DNA repair or response to oxidative stress seem to be able to predict late toxicity. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01316328.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.122
GPT teacher head0.479
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental & Clinical Cancer ResearchSame topicEffects of Radiation ExposureFrench-language works237,207