Clinical management of short children with low serum immunoglobulin but no immunodeficiency features
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".