Do CVID patients on SCIG have more autoimmune (thrombo)cytopenic events than CVID patients on IVIG?
Bibliographic record
Abstract
Autoimmune thrombocytopenia (AITP) is frequent in patients diagnosed with common variable immunodeficiency (CVID). High dose intravenous immunoglobulin treatment (IVIG) has conventionally been a cornerstone of the initial therapy for AITP. This study aimed to assess the safety and effectiveness of subcutaneous immunoglobulin (SCIG) compared to IVIG in preventing AITP in CVID patients. This prospective observational study enrolled 47 adult CVID patients concurrently diagnosed with AITP. Of the participants, 27 (57%) were treated with SCIG, while 20 (43%) received IVIG. AITP episodes were defined as platelet counts <50,000/µl with bleeding or <20,000/µl with or without bleeding, followed over a 64-month period. Among the 47 patients included, 12 (25.5%) experienced AITP episodes, with seven using SCIG and five using IVIG. No significant difference was observed in AITP occurrence between the two treatment groups (p-value=0.99). Neither splenomegaly nor the use of immunosuppressive therapies showed a correlation to the AITP bouts. Maintaining IgG trough levels above 7g/l arose as a key factor for preventing AITP in both treatment modalities. In conclusion, both SCIG and IVIG demonstrated comparable efficacy in the prevention of AITP in CVID patients. This study highlights the importance of monitoring IgG levels in the management of CVID patients with AITP.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".