Assignment of cytomegalovirus infection status in infants awaiting solid organ transplant: Viral detection methods as adjuncts to serology
Bibliographic record
Abstract
Assignment of CMV infection status in infants awaiting SOT is challenging as passive maternal antibody can lead to false-positive serology. Since 2000, our protocol has recommended sending throat and urine samples for CMV viral detection, culture, or NAAT, for CMV-seropositive infants <18 months awaiting SOT. We reviewed pretransplant CMV serology for 152 infants and, for CMV seropositives, examined relationships between CMV IgG OD values, age, and CMV viral detection to explore time to clearance of maternal CMV IgG and evaluate viral detection in assignment of pretransplant CMV infection status. The proportion of CMV-seropositive infants decreased from 52% in infants 0-6 months of age to 28% in those 12-18 months. Among CMV-seropositive infants, median OD was significantly higher in the 6- to 12- and 12- to 18-month groups compared to the 0- to 6-month group. Distribution of OD by age group suggested that maternal antibody cleared before 12 months. Of 59 eligible CMV-seropositive infants, 49 (83%) had CMV viral detection studies and 18 of 49 (36.7%) had detectable CMV: 9 of 30 (30.0%) infants 0-6 months, 7 of 15 (46.7%) infants 6-12 months, and 2 of 4 (50.0%) infants 12-18 months. CMV viral detection studies are useful to confirm positive CMV infection status in CMV-seropositive infants awaiting SOT. Maternal CMV IgG likely clears before 12 months.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".