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Record W4377142708 · doi:10.1177/11795476231173503

Blue Rubber Bleb Nevus Syndrome Presenting as Anemia, Hemorrhage, and Hemangiomas: A Rare Case Report

2023· article· en· W4377142708 on OpenAlexaff
Qaisar Ali Khan, Christopher Farkouh, Arooba Khan, Zahir Uddin, Parsa Abdi, Michelle Anthony, Faiza Amatul Hadi, Eyan Khan, Sara Pervez

Bibliographic record

VenueClinical Medicine Insights Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations and Hemangiomas
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineIron-deficiency anemiaBlue nevusAnemiaAngiomaHemangiomaDermatologyGastrointestinal bleedingAsymptomaticNevusSurgeryVascular diseaseInternal medicine

Abstract

fetched live from OpenAlex

Background: Blue Rubber Bleb Nevus syndrome (BRBNS) is a rare disorder, that results in congenital cutaneous hemangiomas of the skin and gastrointestinal tract. Although asymptomatic, the nevi present as soft, non-mobile, dark blue, compressible papules. Clinically it presents as iron deficiency anemia due to occult gastrointestinal bleeding. Case presentation: A 22-year-old female patient presented with complaints of shortness of breath, fatigue, and palpitation for 2 months. On examination, she had a pale effect and widespread hemangiomas on her lips, hands, and feet. Laboratory results revealed iron deficiency anemia with hemoglobin (Hb) of 2.1 gm/dl and histopathology results of the hemangioma specimen showed angiokeratomas. Based on clinical manifestations and laboratory results, the patient was diagnosed with a case of BRBNS. The patient was transfused with red cell concentrate her symptoms improved but on the first follow-up visit her Hb again dropped to 8.6 mg/dl. Conclusion: A high suspicion of BRBNS diagnosis should be considered if a patient presents with iron deficiency anemia and multiple cutaneous hemangiomas. Further screening should be done to explore internal bleeding and hemangiomas.

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)0.005
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.051
GPT teacher head0.364
Teacher spread0.313 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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