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Record W3206310817 · doi:10.14740/jh906

Hemolytic Anemia Complicating COVID-19 Infection

2021· article· en· W3206310817 on OpenAlexvenueno aff
Abeer N. AbouYabis, Grace Thompson Bell

Bibliographic record

VenueJournal of Hematology · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoagulopathyHemolytic anemiaAnemiaImmunologyAutoantibodyEtiologyImmune systemAutoimmune hemolytic anemiaDiseasePandemicCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)AntibodyInternal medicine

Abstract

fetched live from OpenAlex

Coronavirus disease 2019 (COVID-19) has been associated with a spectrum of reported hematological complications ranging from immune cytopenias to thromboembolic manifestations of coagulopathy. Moreover, there have been documented cases of hemolytic anemia associated with COVID-19 infection which have been mainly attributed to development of autoantibodies. We report a case of an African-American patient who presented with hemolytic anemia in the second week after his COVID-19 diagnosis. Throughout this report, we explore the potential immune and non-immune etiologies that contributed to the patient's hemolytic anemia in the setting of COVID-19 infection guided by a review of literature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.330
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations25
Published2021
Admission routes1
Has abstractyes

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