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Clinical management of short children with low serum immunoglobulin but no immunodeficiency features

2010· article· en· W1547093643 on OpenAlexaff
Cristina Meazza, Paola Cerutti, Sara Pagani, Agata Boncimino, Massimo Marconi, Mauro Bozzola

Bibliographic record

VenuePediatrics International · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsUniversity Hospital Foundation
Fundersnot available
KeywordsHypogammaglobulinemiaMedicineImmunodeficiencyAntibodyFailure to thriveImmunologyPrimary immunodeficiencyImmunoglobulin MImmunoglobulin GLymphocytePediatricsImmune system

Abstract

fetched live from OpenAlex

BACKGROUND: In children of different ages investigated for failure to thrive, low (below the cut-off for age) immunoglobulin (Ig) values can be detected, without any clinical evidence of humoral immunodeficiencies. To better characterize infants presenting with diminished immunoglobulin levels, we studied IgG subclasses, in vitro Ig production and B cell subpopulation. METHODS: We monitored 17 children (12 boys and five girls, age range 1-18 years) with low serum levels of one or more Ig isotypes but without any clinical or laboratory features of immunodeficiency. RESULTS: Low IgM levels were frequent (52.9%). During the follow up, six of 17 cases (35.3%) normalized their immunoglobulin levels. Frequently, in the observed patients, low levels of immunoglobulins were not limited to the period of infancy. In all patients, in vitro Ig production and B lymphocyte subpopulations were within normal ranges. CONCLUSIONS: We suggest a quantification of serum Ig levels in children who fail to thrive in order to identify patients with low Ig levels. These children should be monitored until Ig levels normalize to exclude any immunodeficiency status. Early recognition of children with persistent hypogammaglobulinemia would allow prompt and appropriate clinical interventions.

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 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 score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.257
Teacher spread0.252 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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