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Record W4390605898 · doi:10.1159/000535099

Li-Fraumeni Syndrome with Unusual Synchronous Malignancies: A Case Report

2024· article· en· W4390605898 on OpenAlexaff
Essa Al Mansor, Anna Adamiak, Timothy R. Asmis

Bibliographic record

VenueCase Reports in Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineMicrosatellite instabilityLi–Fraumeni syndromeCancerInternal medicineAdenocarcinomaNeuroendocrine tumorsNivolumabGastroenterologyOncologyImmunotherapyGermline mutationMutation

Abstract

fetched live from OpenAlex

<b><i>Introduction:</i></b> Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder brought on by pathogenic mutations in the <i>TP53</i> tumor suppressor gene. LFS is characterized by a high lifetime risk of developing various cancers at a relatively young age. <b><i>Case Presentation:</i></b> We are presenting a 48-year-old male with a diagnosis of LFS that was confirmed by a genetic test triggered by the patient’s son’s diagnosis of LFS and leukemia. The patient’s main symptoms were abdominal pain and weight loss. The patient was diagnosed with two synchronous primary tumors: first, a metastatic gastric invasive adenocarcinoma that is microsatellite instability (MSI) -high; and second, a low grade (G1) (non-function) well-differentiated pancreatic neuroendocrine tumor. These cancers are not the usual type associated with LFS. After eight cycles of chemo-immunotherapy in the form of FOLFOX-Nivolumab, our radiological assessment showed significant response in the metastatic gastric adenocarcinoma and stable disease in pancreatic neuroendocrine tumor. The patient remains on single agent Nivolumab and has had stable disease for the last 12 months. <b><i>Conclusion:</i></b> Gastric cancer and neuroendocrine tumors are not usually associated with LFS. This case illustrates a rare clinical presentation of multiple malignancies in LFS 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.307
Teacher spread0.293 · 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 designCase report
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

Citations2
Published2024
Admission routes1
Has abstractyes

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