A New Prognostic Score Based on Cell-Mediated Immunity for Cytomegalovirus Infection After Transplantation
Bibliographic record
Abstract
Introduction: The interferon gamma (IFN-γ) enzyme-linked immunosorbent spot is a highly sensitive immune assay that enables the assessment of cytomegalovirus (CMV)-specific cell-mediated immunity (CMI) and can identify at-risk transplant patients of CMV infection; however, its clinical implementation remains elusive. Methods: We developed a novel CMV-CMI risk-score based on the standardized T-SPOT.CMV assay against 2 CMV antigens (immediate-early protein 1 [IE-1] and 65 kDa phosphoprotein [pp65]), a biomarker predicting CMV infection, both high viral replication, and disease by performing a pooled analysis of 570 kidney transplants participating in different clinical trials and subsequently validating it in 146 consecutives solid organ transplants (SOT) in an interventional trial. By incorporating clinical variables into the CMV-CMI risk-score, we built an integrative prognostic system quantifying the risk of CMV infection (CMV-PrognosTIC score) using elastic net penalized regression analysis. Results: < 0.0001, respectively), by combining both responses, 3 CMV-CMI risk-scores appeared, accurately discriminating low-risk (LR) from intermediate-risk (IR) and high-risk (HR) patients (98.7% negative predictive value [NPV], 97.2% sensitivity). Its prospective implementation guiding decision-making in an independent SOT cohort confirmed the very high NPV and sensitivity identifying LR patients. By integrating type of preventive therapy, patient age, and donor (D) and recipient (R) CMV-serostatus to the CMV-CMI risk-score, we generated a global risk-prognostic model showing accurate discrimination and calibration in both derivation (AUC: 0.807) and validation cohorts (AUC: 0.719). Conclusion: We developed a robust CMV-PrognosTIC score to quantify the risk of CMV infection in SOT, which may be readily implemented in clinical transplantation to personalize CMV preventive therapies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".