Common Variable Immune Deficiency in Pregnancy: Multidisciplinary Approach to Improving Obstetrical Outcome
Bibliographic record
Abstract
not-yet-known not-yet-known not-yet-known unknown Background: Pregnancy poses unique challenges for women with Common Variable Immune Deficiency (CVID), requiring tailored immunological and obstetrical management. Presently, there are no guidelines available to guide manage of such patients. Standardizing care for patients with CVID who are pregnant or contemplating pregnancy is important to optimize maternal and fetal outcomes. Methods: A systematic review of the literature was performed to guide clinical management recommendations. A retrospective chart review of women with CVID who became pregnant while receiving care at the McGill University Health Centre between 2015–2025 was conducted. Maternal, obstetrical, immunological, and immediate neonatal outcomes are described. Results: Key management principles included trimester-based immunoglobulin (IgRT) dose adjustments, regular monitoring of serum IgG, proactive management of autoimmune or infectious complications, and multidisciplinary coordination. SCIG therapy offered flexibility and stability of IgG levels. Conclusion: Pregnancy in women with CVID can result in favorable outcomes when managed with structured IgRT protocols, regular surveillance, and integrated multidisciplinary care. We propose a management algorithm based on available literature and our single center experience. This can be used as a framework for future prospective studies which are needed to refine management strategies.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".