Evaluation of a Novel Global Immunity Assay to Predict Infection in Organ Transplant Recipients
Bibliographic record
Abstract
Background: Solid organ transplant recipients (SOTRs) are predisposed to infection due to the need for lifelong immunosuppression, although tools to measure the overall degree of immunosuppression are limited. In this study, we used a novel global cell-mediated immunity (CMI) assay to quantify the degree of immunosuppression and predict subsequent infections. Methods: Consecutive SOTRs were enrolled and provided whole blood to conduct the global CMI assay (QuantiFERON Monitor) at 1, 3, and 6 months posttransplant. The assay measures plasma interferon gamma (IFN-γ) levels after stimulation of whole blood with antigens that stimulate both innate and adaptive immunity. Bacterial, viral, and fungal infections were prospectively recorded. Results: We enrolled 137 patients who provided CMI measurements on at least 1 study timepoint. Median age was 58 years; transplant types were kidney (32.1%), liver (30.7%), and lung (36.5%). At least 1 episode of infection occurred in 32 of 137 (23.4%) patients between 1 and 3 months, 34 of 135 (25.1%) between 3 and 6 months, and 39 of 132 (29.5%) between 6 and 12 months. IFN-γ levels were significantly lower in those with at least 1 episode of infection vs no infection at month 1 (P = .04), month 3 (P = .05), and month 6 (P = .006). Patients who developed opportunistic infections (OIs) also showed a significantly lower CMI than those without OI at months 3 and 6. Using a cutoff value of ≤10 IU/mL of IFN-γ, there was a 2- to 3-fold greater likelihood of subsequent infection in those with lower CMI. Conclusions: We show that a novel global immunity assay is able to quantify the level of immunosuppression and predict the risk of subsequent infection episodes in organ transplant recipients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".