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Record W4390452114 · doi:10.1016/j.adro.2023.101433

Urticaria Heralding Breast Cancer: Case Report and Literature Review

2023· article· en· W4390452114 on OpenAlexaff
Benjamin Royal-Preyra, Melanie Boucher, Isabelle Marsan

Bibliographic record

VenueAdvances in Radiation Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicUrticaria and Related Conditions
Canadian institutionsUniversité de MontréalUniversité du Québec à Trois-RivièresCentre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec
Fundersnot available
KeywordsMedicineBreast cancerCancerMalignancyDermatologyInternal medicinePathologyOncology

Abstract

fetched live from OpenAlex

Paraneoplastic syndromes are relatively rare and occur due to aberrant immune, endocrine, or metabolic stimulation by cancer cells 1 and cause symptoms due to the generation of autoantibodies, cytokines, hormones, or peptides rather than direct tumor infiltration or metastasis 2-3 . Paraneoplastic syndromes are estimated to affect up to 8% of patients with cancer 3 . Treatment of a paraneoplastic syndrome involves treating the underlying malignancy, which usually, but not always, leads to the resolution of a patient's symptoms 4 . Paraneoplastic manifestations of breast cancer, including hypercalcemia, Sweet syndrome, dermatomyositis, and granulocytosis, are reported in the literature 5 . Urticaria as a paraneoplastic syndrome of breast cancer has only been reported three times in the English literature 5-7 . This report describes the case of a 43-year-old woman presenting with a 3-year history of monoclonal antibody refractory chronic diffuse urticaria that preceded the diagnosis of, and only resolved after treatment of, a luminal A breast cancer with surgery and adjuvant radiation. A review of the published literature on urticaria in breast cancer patients is also presented and discussed. Clinicians should be aware that breast cancer can present with generalized urticaria refractory to medical management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.393
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.369
Teacher spread0.359 · 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 designCase report
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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