The utility of a monocyte monolayer assay in the assessment of <scp>intravenous immunoglobulin</scp>–associated hemolysis
Bibliographic record
Abstract
BACKGROUND: Hemolysis following the administration of intravenous immunoglobulin (IVIG) is an important adverse event (AE). While the monocyte monolayer assay (MMA) has been used to predict in vivo hemolysis when serologically incompatible blood may be transfused, it has also been shown to correlate with IVIG-associated hemolysis. In this study, the MMA was examined for its utility in assessing the risk of hemolysis after IVIG. STUDY DESIGN AND METHODS: Forty-two non-blood group O patients receiving high-dose IVIG (≥2 g/kg) were examined using an autologous and allogeneic MMA. Hemolysis was defined by a drop in hemoglobin of ≥1 g/L, a positive direct antiglobulin test (DAT) and eluate, and a decrease in haptoglobin or increase in lactate dehydrogenase and/or reticulocytes. RESULTS: Forty-two patients provided 50 assessable postinfusion samples, with hemolysis observed in 20 (40%) of cases. Autologous MMA using post-IVIG red blood cells significantly correlated with clinical outcomes when compared to allogeneic MMA (P = .0320 vs .5806, t test). No significant difference in receiver operating characteristics was observed when comparing autologous MMA testing against DAT for the diagnosis of IVIG-associated hemolysis. However, when using samples collected 5 to 10 days after receipt of high-dose IVIG, the autologous MMA had higher sensitivity than the DAT. CONCLUSION: MMA testing with autologous monocytes collected 5 to 10 days after receipt of high-dose IVIG can be used for the diagnosis of IVIG-associated hemolysis and may be of particular value in cases in which the Day 5 to 10 DAT is negative.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".