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Record W2121777182 · doi:10.14740/jh222w

A Case of Evans Syndrome: A Clinical Condition With Under-Recognized Thrombotic Risk

2015· article· en· W2121777182 on OpenAlexvenueno aff
Zachary Otaibi, Rohit Rao, Santhosh Sadashiv

Bibliographic record

VenueJournal of Hematology · 2015
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
Fundersnot available
KeywordsEvans syndromeMedicineImmunologyAntibody

Abstract

fetched live from OpenAlex

Evans syndrome (ES) is a rare hematologic disorder characterized by the presence of Direct Antiglobulin test (DAT) positive autoimmune hemolytic anemia (AIHA), immune thrombocytopenia (ITP), and/or immune neutropenia. The risk of thrombosis has been well established in retrospective studies for ITP and AIHA; however, the risk of thrombosis in ES has been limited to case reports and individual case series. Whether the risk of thrombosis in ITP and AIHA is additive has not been well established, but preliminary observation suggests the rate of thrombosis is higher than the rate observed in ITP or AIHA individually. Furthermore, ES appears to be an underappreciated diagnosis. Anemia in the presence of ITP is often considered to be secondary to acute blood loss and a proper workup for hemolysis is often missed. Appropriate risk stratification of these patients is hindered by this lack of workup and many patients are unfortunately not treated appropriately and/or not offered appropriate prophylaxis for their level of thrombotic risk. The purpose of this case report is to not only increase the level of awareness among clinicians to initiate an appropriate diagnostic workup in patients presenting with anemia in the setting of ITP, but also to a heightened thrombotic risk warranting an appropriate thromboprophylaxis. This case also adds to the body of evidence that a “second- hit” phenomenon is often a precipitating cause for a thrombotic event. J Hematol. 2015;4(3):205-209 doi: http://dx.doi.org/10.14740/jh222w

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.060
GPT teacher head0.363
Teacher spread0.304 · 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
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
Published2015
Admission routes1
Has abstractyes

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