Clinical manifestation for immunoglobulin A deficiency: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Immunoglobulin A deficiency (IgAD) is a common disease with an unknown genetic defect, characterized by the decreased or absent IgA with other isotypes normal, normal subclasses, and specific antibodies. Patients with this disorder represent a spectrum of clinical manifestations including infections, autoimmune disorders, malignancy, and allergic diseases. The current study aimed to evaluate their prevalence and categorized them. METHODS: We searched PubMed, Web of Science, and Scopus databases to find eligible studies from the earliest available date to January 2022 with standard keywords. Pooled estimates of clinical manifestations prevalence and the corresponding 95% confidence intervals were calculated using random-effects models. RESULTS: The most prevalent clinical manifestations belonged to infection (64.8%) followed by allergic diseases (26.16%) and autoimmunity (22.0%), respectively. In selective IgA deficiency patients as the largest group of IgAD in current study, celiac disease (6.57%), Inflammatory bowel disease (4.01%), and rheumatoid arthritis (3.80%) were the most prevalent autoimmunity. Meanwhile, the most frequent infection was respiratory tract infection, fungal infection, and gastrointestinal infection at 50.74%, 18.48%, and 15.79%, respectively. In addition, the pooled prevalence of asthma, allergic rhinitis, and allergic conjunctivitis were 19.06%, 15.46%, and 11.68%, respectively which were reported as the most widespread allergic diseases. CONCLUSIONS: Our results showed that apart from undiagnosed IgAD patients, IgAD patients represent a wide range of clinical manifestations. Infection, allergy, and autoimmunity are the most common clinical manifestations. The concurrent presence of IgA and IgG subtypes deficiency could be associated with increased susceptibility to infection. Considering the probability of developing new clinical complications during follow-up, periodic assessments of IgAD patients should be inspected.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".