Clinical outcomes of polyvalent immunoglobulin use in solid organ transplant recipients: A systematic review and meta‐analysis – Part II: Non‐kidney transplant
Bibliographic record
Abstract
Immunoglobulin (IG) is commonly used to desensitize and treat antibody-mediated rejection in solid organ transplant (SOT) recipients. The impact of IG on other outcomes such as infection, all-cause mortality, graft rejection, and graft loss is not clear. We conducted a similar systematic review and meta-analysis to our previously reported Part I excluding kidney transplant. A comprehensive literature review found 16 studies involving the following organ types: heart (6), lung (4), liver (4), and multiple organs (2). Meta-analysis could only be performed on mortality outcome in heart and lung studies due to inadequate data on other outcomes. There was a significant reduction in mortality (OR 0.34 [0.17-0.69]; 4 studies, n = 455) in heart transplant with hypogammaglobulinemia receiving IVIG vs no IVIG. Mortality in lung transplant recipients with hypogammaglobulinemia receiving IVIG was comparable to those of no hypogammaglobulinemia (OR 1.05 [0.49, 2.26]; 2 studies, n = 887). In summary, IVIG targeted prophylaxis may decrease mortality in heart transplant recipients as compared to those with hypogammaglobulinemia not receiving IVIG, or improve mortality to the equivalent level with those without hypogammaglobulinemia in lung transplant recipients, but there is a lack of data to support physicians in making decisions around using immunoglobulins in all SOT recipients for infection prophylaxis.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".