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Record W2150980760 · doi:10.1186/1710-1492-8-s1-a23

Experience with subcutaneous immunoglobulin therapy in two pediatric cases of immune thrombocytopenia purpura

2012· article· en· W2150980760 on OpenAlexaffvenue
Hugo Chapdelaine, Hélène Decaluwe, MC Levasseur, F De Deist, Élie Haddad

Bibliographic record

VenueAllergy Asthma and Clinical Immunology · 2012
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineHypogammaglobulinemiaSplenectomyIntravenous Immunoglobulin TherapyThrombocytopenic purpuraVomitingPrednisoneNauseaPurpura (gastropod)SurgeryAntibodyInfusion therapyPediatricsPlateletAnesthesiaInternal medicineImmunology

Abstract

fetched live from OpenAlex

Background Immune thrombocytopenia purpura (ITP) can co-exist with primary immunodeficiencies. Intravenous immunoglobulin (IGIV) therapy is an effective treatment. Subcutaneous immunoglobulin (SCIG) formulations that can be home-delivered have recently been developed. We describe 2 cases of pediatric ITP associated with hypogammaglobulinemia, treated with SCIG. Case description Case 1 A 14-year old male presented with a symptomatic thrombopenia. Infusions of IGIV led to an immediate improvement in platelet count. However, he experienced post-infusion intractable headaches, nausea and vomiting, which recurred after subsequent infusions. Intravenous anti-D therapy resulted in a severe allergic reaction. Short course prednisone protocol was implemented for symptomatic episodes. Preliminary blood work for splenectomy revealed low IgG level. The patient was put on SCIG replacement therapy (116 mg/kg/week). He experienced only one relapse since, which remained corticoresponsive. Case 2

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.342
Teacher spread0.314 · 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
Published2012
Admission routes2
Has abstractyes

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