Probabilistic Modeling of Cytomegalovirus Infection Under Consensus Clinical Management Guidelines
Bibliographic record
Abstract
BACKGROUND: Cytomegalovirus (CMV) is the most common viral pathogen after renal transplantation and remains a major therapeutic challenge with important clinical and economic implications from both direct and indirect consequences of infection. METHODS: This 5-year study modeled the relationship between CMV infection and biopsy-proven graft rejection, graft loss, or death after renal transplantation in an inception cohort using Canadian consensus guidelines for CMV management as a component of a detailed cost-analysis of viral infection. RESULTS: Probabilities of CMV viremia and syndrome/disease among 270 sequential graft recipients were 0.27 and 0.09, respectively; 91% of cases occurred in the first 6 months. Probability of CMV infection as the first event was 0.29, with a probability of subsequent biopsy-proven acute rejection (BPAR) of 0.05 (mean: 62+/-26 days, range: 32-85 days), whereas the probability of BPAR as the first event was 0.18, with a probability of subsequent CMV infection of 0.38 (mean: 63+/-31, range: 27-119 days). Probability of freedom from both CMV infection and BPAR throughout the period of observation was 0.53. Time-dependent Cox analysis showed that neither donor/recipient CMV risk stratum nor CMV infection influenced the risks of BPAR (P=0.24; P=0.74) or of graft loss or death (P=0.26; P=0.34). In contrast, BPAR significantly increased the risk of both subsequent CMV infection (hazard ratio=1.77, P=0.03) and of graft loss or death (hazard ratio=8.31, P<0.0001). CONCLUSIONS: Although current antiviral therapy seems to mitigate the reported deleterious effects of CMV infection on BPAR or graft survival, BPAR remains a significantly risk factor for both CMV infection and functional graft survival.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".