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Record W2127767332 · doi:10.1186/1897-4287-10-3

The R337H mutation in TP53 and breast cancer in Brazil

2012· article· en· W2127767332 on OpenAlexaff
Magda CB Gomes, Joanne Kotsopoulos, Gutemberg Almeida, Maurício Magalhães Costa, Roberto José da Silva Vieira, Firmino de AG Filho, Marcos Bettini Pitombo, Paulo Roberto Falcão Leal, Robert E. Royer, Phil Zhang, Steven A. Narod

Bibliographic record

VenueHereditary Cancer in Clinical Practice · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsWomen's College Hospital
Fundersnot available
KeywordsMedicineBreast cancerGermline mutationLi–Fraumeni syndromePenetranceOncologyMutationCancerPopulationIncidence (geometry)Internal medicineFamily historyMutation frequencyGeneticsEnvironmental healthBiologyGene

Abstract

fetched live from OpenAlex

BACKGROUND: Germline mutations in p53 are associated with the Li-Fraumeni Syndrome which is characterized by childhood cancers, including pediatric adrenal cortical carcinomas and early onset breast cancer. The high incidence of adrenal cortical carcinomas in southern Brazil is mostly attributed to the R337H mutation in TP53. The relatively high population frequency of this mutation in southern Brazil, along with the clustering of early onset breast cancer in Li-Frameni families, suggests this mutation may also be a low-penetrance breast cancer susceptibility polymorphism. METHODS: We undertook this study to evaluate the frequency of the R337H mutation in breast cancer patients from Rio de Janeiro, Brazil. R337H mutation status was determined in 390 unselected breast cases and 324 controls identified from clinics in Rio de Janeiro, Brazil using a PCR-based assay. RESULTS: Two of the breast cancer cases (0.5%) and none of the controls carried the mutation. Both cases had an early age at diagnosis (< 40 years old) and a family history of breast and other cancers. CONCLUSIONS: These data suggest genetic screening of young onset breast cancer patients should include testing for the R337H mutation.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
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 head0.042
GPT teacher head0.433
Teacher spread0.391 · 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

Citations41
Published2012
Admission routes1
Has abstractyes

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