Effect of COVID-19 on neuromotor development of infants: A case control study
Bibliographic record
Abstract
Effects of the COVID-19 pandemic on neuromotor development of infants is not clear yet. In this study, based on the hypothesis that COVID-19 may cause delay in neuromotor development in infants, it was aimed to evaluate and compare the neuromotor development of 6-18 month-old infants who were hospitalized for COVID-19 and healthy children. All of the children were assessed at one point by the pediatric neurologist and pediatric physiotherapist. The Denver II developmental screening test (DDST), Hammersmith Infant Neurological Examination (HINE) test, and Alberta Infant Motor Scale (AIMS) were used to determine the neurodevelopmental status of the infants. SARS-CoV-2 PCR positive 27 children (14 boys, 13 girls) and 29 healthy children (15 boys, 14 girls) were included in the study. The clinical findings of most of the patients were mild. The mean age of the patients was 11.4±4.2 months and the mean length of hospital stay was 4.6±2.4 days. The most common symptom was fever in 19 patients (70%) followed by cough (25.9%), diarrhea (25.9%), vomiting (18.5%), loss of appetite (14.8%), myalgia (11.1%), dyspnea (3.7%), and sore throat (3.7%). No significant feature was detected in laboratory and imaging findings. The DDST was abnormal in 3 COVID+ patients: one of them was a 6-month-old patient with a birth history of asphyxia and the others were age-retarded in language and social development with no chronic disease. There was no significant difference in the HINE and AIMS tests. In small-scale and short-term follow-up, we found that COVID-19 did not inhibit the neuromotor development of infants except for slight retardation in language and social development. We think there is a need for more comprehensive prospective studies including patients with moderate and severe infections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".