Diagnosis of Cytomegalovirus infection in a very low birth weight infant using metagenomic next-generation sequencing: A case report
Bibliographic record
Abstract
RATIONALE: Cytomegalovirus (CMV) is a DNA virus from the herpesvirus family that is widespread among humans. Very low birth weight infants (VLBWI) are particularly susceptible to postnatal CMV infection due to their compromised immune systems. The clinical manifestations of postnatal CMV infection are often nonspecific, which complicates early detection and may lead to multi-organ dysfunction and long-term sequelae. PATIENT CONCERNS: A VLBWI developed unexplained persistent fever during hospitalization. Conventional diagnostic methods, including routine microbiological tests, failed to identify the causative pathogen. DIAGNOSES: Metagenomic next-generation sequencing (mNGS) was performed and successfully identified CMV as the etiologic agent. Traditional diagnostic approaches were insufficient, but mNGS provided a comprehensive analysis of microbial nucleic acids, leading to a definitive diagnosis. INTERVENTIONS: The patient received antiviral treatment with ganciclovir following the identification of CMV by mNGS. OUTCOMES: After antiviral therapy, the fever resolved, and no long-term sequelae were observed during follow-up. LESSONS: This case demonstrates the efficacy of mNGS as a powerful diagnostic tool for identifying the causes of unexplained infections in VLBWI. Compared with conventional methods, mNGS offers significant advantages, particularly in detecting a wide range of pathogens simultaneously. The successful diagnosis and treatment in this case underscore its clinical utility in managing complex neonatal infectious diseases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| 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".