A Sequential Two-Step Cell-Based Assay Predicts Immunosuppression-Related Adverse Events
Bibliographic record
Abstract
Abstract Immunosuppressants are associated with serious and often life-threatening adverse effects. To optimize immunotherapy, a tool that measures the immune reserve is necessary. We validated that a cell-based assay that measures TNF-α production by CD14+16+ intermediate monocytes following stimulation with EBV peptides has high sensitivity for the detection of over-immunosuppression (OIS) events. To develop a sequential, two-step assay with high specificity, we used PBMCs from kidney recipients (n = 87). Patients were classified as cases or controls, according to the occurrence of opportunistic infection, recurring bacterial infections, or de novo neoplasia. Patients who tested positive in the first step were randomly allocated to a training or a testing set for the development of the second step. In the discovery phase, an assay based on the examination of early mature B (eBm5) cells was able to discriminate OIS patients from controls with a specificity of 88%. The testing set also revealed a specificity of 88%. The interassay coefficient of variability between the experiments was 6.1%. Stratified analyses showed good diagnostic accuracy across tertiles of age and time posttransplant. In the adjusted model, the risk of OIS was more than 12 times higher in patients classified as positive than in those who tested negative (adjusted hazard ratio, 12.2; 95% confidence interval: 4.3–34.6). This sequential cell-based assay, which examines the monocyte and eBm5 cell response to EBV peptides, may be useful for identifying OIS in immunosuppressed patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".