The Direct and Indirect Impact of SARS-CoV-2 Infections on Neonates
Bibliographic record
Abstract
BACKGROUND: The impact of SARS-CoV-2 on neonates remains largely unknown in low- and middle-income countries (LMICs). We provide an epidemiologic and clinical report of SARS-CoV-2 infections in neonates hospitalized in Bangladesh. METHODS: Outborn neonates admitted to Dhaka Shishu Hospital, a tertiary-care referral hospital, between 29 March and 1 July were screened for SARS-CoV-2. We reviewed clinical data, including chest radiograph and laboratory reports, and conducted SARS-CoV-2 genome sequencing. Patients were followed-up for 27-75 days. A subset of caregivers was also tested. RESULTS: Of 83 neonates tested, 26 were positive (median age 8 days). Most neonates were admitted with diagnosis unrelated to SARS-CoV-2: 11 presented with serious non-communicable diseases, 7 with early-onset sepsis, 5 with late-onset sepsis and 2 with pneumonia. In 3 of 5 chest radiograph, infiltrates and ground-glass or patchy opacities were noted. Two neonates developed metabolic acidosis, one developed disseminated intravascular coagulation. Most SARS-CoV-2 positive neonates were referred to government-designated COVID-19 hospitals, leading to gaps in treatment. Twenty-three neonates could be followed-up: 12 were healthy, 8 died and 3 were still seeking medical care. Of 9 caregivers tested, 8 were positive. CONCLUSIONS: SARS-CoV-2 may have serious adverse effects on children born in LMICs. The virus likely contributed directly to two deaths, but the remaining 6 neonates who died had serious comorbidities. Positive SARS-CoV-2 test results led to gaps in immediate clinical care for other morbidities, which likely contributed to adverse outcomes. This case series emphasizes the need to understand COVID-19 in neonates in LMICs and its indirect impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".