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Record W2594796471 · doi:10.1101/cshperspect.a026187

Inherited<i>TP53</i>Mutations and the Li–Fraumeni Syndrome

2017· review· en· W2594796471 on OpenAlexaff
Tanya Guha, David Malkin

Bibliographic record

VenueCold Spring Harbor Perspectives in Medicine · 2017
Typereview
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsLi–Fraumeni syndromeGermlineContext (archaeology)Germline mutationEpigenomeCancerGeneticsDiseaseMutationMedicineBiologyOncologyInternal medicineGeneDNA methylation

Abstract

fetched live from OpenAlex

Li -Fraumeni syndrome (LFS) is a complex hereditary cancer predisposition disorder associated with early-onset cancers in diverse tissues of origin. Germline TP53 mutations are identified in 75% of patients with classic LFS. The lifetime likelihood of a TP53 mutation carrier developing cancer approaches 75% in males and almost 100% in females. Several genetic modifiers have been implicated to account for the phenotypic variability within and across LFS families; however, efforts to develop predictive algorithms of age of onset and type of cancers in individual patients have not yet found clinical use. Although it is not possible to prevent cancers from forming in LFS patients, novel protocols have been developed for surveillance for early tumor detection, leading to improvements in survival. Comprehensive studies of the genome and epigenome in LFS families in the context of germline TP53 mutations is anticipated to shed light on this intriguing, yet devastating, disease and to transform the clinical management of patients.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.075
GPT teacher head0.371
Teacher spread0.296 · 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

Citations254
Published2017
Admission routes1
Has abstractyes

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