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Record W4221053386 · doi:10.1186/s13223-022-00666-2

Hypereosinophilic syndrome presenting as coagulopathy

2022· article· en· W4221053386 on OpenAlexvenueno aff
Kestutis Aukstuolis, Jocelyn J. Cooper, Katherine Altman, Anna Lang, Andrew G. Ayars

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Disorders and Syndromes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHypereosinophiliaEosinophiliaHypereosinophilic syndromeBenralizumabCoagulopathyInternal medicineGastroenterologyAbdominal painDermatologySurgeryMepolizumabEosinophilAsthma

Abstract

fetched live from OpenAlex

BACKGROUND: Hypereosinophilic syndrome (HES) is an extremely uncommon group of disorders. It rarely presents with coagulopathy without cardiac involvement. CASE PRESENTATION: A 33-year-old previously healthy male with no history of atopic disease presented with abdominal pain, hematochezia, peripheral eosinophilia as high as 10,000 eos/µL, right and left portal vein, mesenteric, and splenic vein thrombi with ischemic colitis resulting in hemicolectomy and small bowel resection. Despite an extensive workup for primary and secondary etiologies of hypereosinophilia by hematology/oncology, infectious disease, rheumatology and allergy/immunology, no other clear causes were identified, and the patient was diagnosed with idiopathic HES. His eosinophilia was successfully treated with high-dose oral corticosteroids (OCS) and subsequently transitioned to anti-IL-5-receptor therapy with benralizumab. He has continued this treatment for over a year with no recurrence of eosinophilia or thrombosis while on benralizumab. CONCLUSION: In patients with an unexplained coagulopathy and eosinophilia, eosinophilic disorders such as HES should be considered. Corticosteroid-sparing agents, such as benralizumab show promise for successfully treating these 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.303
Teacher spread0.284 · 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 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

Citations9
Published2022
Admission routes1
Has abstractyes

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