Long-Term Growth and Neurodevelopmental Outcomes of Neonates Infected with SARS-CoV-2 during the COVID-19 Pandemic at 18–24 Months Corrected Age: A Prospective Observational Study
Bibliographic record
Abstract
INTRODUCTION: During the early coronavirus disease (COVID-19) pandemic in 2020, researchers cautioned about the potential neuroinvasive capability of the virus and long-term neurological consequences. Although a few preliminary studies have found delayed communication, fine motor, and problem-solving skills in infants after COVID-19 infection, there continues to be a paucity of data on long-term development of neonates diagnosed with COVID-19. METHODS: We conducted a prospective study of 20 neonates who acquired severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection during the first wave of the pandemic (April-July 2020). At 18-24 months corrected age, we assessed neurodevelopment by Bayley Scales of Infant and Toddler Development, the third edition (BSID-III), along with growth, hearing, and vision evaluation. RESULTS: The mean corrected age at assessment was 21 months 11 days ± 1 month 28 days. We found developmental delay in nearly half of the children with scores below one standard deviation in either of the BSID-III domains. Mild delay in either motor, cognitive, or language domains was found in 9 (45%) children and moderate delay in 2 (10%). Expressive language, fine motor, and receptive language were predominantly affected. None of the children had hearing impairment, blindness, or significant growth faltering including clinically severe microcephaly. The mean composite cognitive, language, and motor scores were significantly lower in those with neurodevelopmental delay (p value - 0.02, 0.000, and 0.03, respectively) without any differences in their disease characteristics. CONCLUSION: Neonates infected with SARS-CoV-2 have an increased risk of developmental delays in expressive language, fine motor, and receptive language skills at 18-24 months of age. The severity of delays is predominantly mild.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".