Trends in Congenital Cytomegalovirus: A Review of Current Screening Methods and Prevention Strategies
Bibliographic record
Abstract
Cytomegalovirus (CMV) is one of the leading causes of congenital sensorineural hearing loss. Currently, ∼40,000 infants in the United States are infected annually with CMV, and of these 40,000 infants, upward of 6000 infants will develop sensorineural hearing loss. Most of these infants will go undetected for congenital hearing loss by a newborn screening program, due to having late-onset or progressive hearing loss. An efficient CMV screening program of newborns will help to identify those infected and at risk for developing sensorineural hearing loss. Also, it will allow close monitoring of these infants for maximum speech and language development. Reliable methods are needed for an effective CMV screening program. Because the dried blood spot (DBS) sample is routinely collected at birth for metabolic screenings, there is growing interest to adapt this as the universal screening method. However, sensitivity of the DBS in detecting CMV has varied, and recent evidence has shown it less reliable than urine or saliva analysis. Further research is needed to develop the most efficient, reliable, and cost-effective programs. Models such as the Quebec metabolic screening program may ensure earlier identification of CMV. We are closer to a means of prophylactic prevention with a CMV vaccine; however, increased patient education of CMV prevention by health care professionals, including audiologists, is the current best practice for reducing the incidence of CMV infection. KEYWORDS Cytomegalovirus - dried blood spot - prevention - congenital sensorineural hearing loss
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".