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Record W4385464262 · doi:10.14740/jmc4126

Intravenous Immunoglobulin-Associated Severe Hemolytic Anemia

2023· article· en· W4385464262 on OpenAlexvenueno aff
Ojbindra KC, Ananta Subedi, Rakshya Sharma

Bibliographic record

VenueJournal of Medical Cases · 2023
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAutoimmune hemolytic anemiaSubclinical infectionAnemiaHemolysisNeutropeniaAdverse effectGuillain-Barre syndromeHemolytic anemiaAntibodyImmunologyInternal medicineToxicity

Abstract

fetched live from OpenAlex

Intravenous immunoglobulin (IVIG) is used to treat immunodeficiency conditions, neuro-immunological, infection-related, autoimmune, and inflammatory disorders and is typically well tolerated. A hematological adverse reaction such as hemolytic anemia and neutropenia is known to occur with IVIG, which is usually transient and subclinical. However, severe hemolytic anemia is known to occur in some cases. We present a case of a 66-year-old man who developed severe symptomatic hemolytic anemia after receiving IVIG for acute inflammatory demyelinating polyneuropathy (AIDP). The patient had known risk factors such as non-O blood group, high cumulative dose of IVIG, and underlying autoimmune condition, which would have put him at high risk for developing hemolytic anemia after IVIG. Therefore, it is prudent for clinicians to have increased awareness regarding the potential for severe hemolysis and closely monitor these patients with risk factors after treatments to identify this adverse reaction before more severe complications occur.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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