Droplet Digital PCR (ddPCR) Does Not Enhance the Sensitivity of Detection of Cytomegalovirus (CMV) DNA in Newborn Dried Blood Spots Evaluated in the Context of Newborn Congenital CMV (cCMV) Screening
Bibliographic record
Abstract
Congenital cytomegalovirus (cCMV) infection is a leading cause of sensorineural hearing loss (SNHL) and neurodevelopmental disabilities in children worldwide. Some regions in the United States and Canada have implemented universal newborn screening for cCMV, which requires molecular diagnostic technologies for identifying cCMV, such as PCR testing of newborn dried blood spots (DBS). This study aimed to evaluate the sensitivity of droplet digital PCR (ddPCR) compared to quantitative real-time PCR to detect CMV DNA in newborn DBS. The limit of detection of various ddPCR primer/probe combinations (singleplex UL55-HEX, singleplex UL83-FAM, and multiplex UL55-HEX/UL83-FAM) was evaluated using the National Institute of Standards and Technology's (NIST) CMV quantitative standard. Singleplex UL55-HEX ddPCR exhibited the lowest limit of detection among the primer/probe combinations tested for ddPCR. UL55 ddPCR was then compared to real-time PCR in 49 infants with confirmed cCMV identified through newborn screening for CMV in saliva swabs and confirmed by a urine test. The results showed that ddPCR was only positive for 59% (29 out of 49) of the cCMV infants, while real-time PCR was positive for 80% (39 out of 49). Due to its lower sensitivity and throughput, ddPCR may not be suitable for cCMV newborn screening.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".