Clinical outcomes of polyvalent immunoglobulin use in solid organ transplant recipients: A systematic review and meta‐analysis
Bibliographic record
Abstract
Polyvalent immunoglobulin is commonly used for desensitization and treatment of antibody-mediated rejection in kidney transplantation but its impact on other outcomes is not known. This systematic review investigated the impact of immunoglobulin prophylaxis on infection, rejection, graft loss, and death following kidney transplantation. A comprehensive literature search located 18 studies (n = 8 randomized controlled trials). None examined the effect of immunoglobulin prophylaxis in transplant recipients with hypogammaglobulinemia. Quality of included studies was variable with high to very high risk of bias. In the randomized trials, immunoglobulin use did not reduce cytomegalovirus infection (OR 0.68 [0.39, 1.21]; 6 studies, n = 295), rejection (OR 0.96 [0.50, 1.82]; 4 studies, n = 187), or graft loss (OR 1.03 [0.46, 2.30]; 6 studies, n = 265). In non-randomized studies, immunoglobulin did not reduce cytomegalovirus infection (OR 0.63 [0.20, 1.94]; 6 studies, n = 361) or death (OR 1.32 [0.05, 38.79]; 3 studies, n = 222) but reduce rejection (OR 0.47 [0.24, 0.94]; 4 studies, n = 268) and graft loss (OR 0.15 [0.05, 0.43]; 2 studies, n = 118). Data were scarce and sample size of current evidence was small. Adequately powered randomized trials are needed to determine if immunoglobulin is an effective intervention to reduce infection, rejection, graft loss, or death following kidney transplantation with and without hypogammaglobulinemia.
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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 | 0.028 |
| Bibliometrics | 0.004 | 0.006 |
| 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) | 0.003 | 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".