Feasibility and reliability to assess the motor development of infants exposed to gestational COVID-19 using the Alberta Infant Motor Scale remotely
Bibliographic record
Abstract
The virus infection severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during pregnancy is a risk factor for developmental problems. Our objectives were to explore feasibility measures and verify the reliability of synchronously employing the Alberta Infant Motor Scale (AIMS) remotely in infants with prenatal exposure to SARS-CoV-2. Additionally, we explored the motor performance of these infants relative to an unexposed normative sample. An exploratory cross-sectional study was carried out and included 20 infants (10.65±4.99 months) whose mothers tested positive for coronavirus disease 2019 (COVID-19) during pregnancy. Infants were assessed with the AIMS remotely and synchronously via video call by a physical therapist. The calls were recorded. Three independent observers scored the recordings. Parents and assessors answered questions regarding barriers to and facilities for the assessments. A higher proportion of parents (90%) found it easy to understand and replicate the commands provided by the therapist during the assessment (P<0.001). The assessors reported not encountering difficulty in most assessments. Interobserver reliability was good in the standing posture [95% confidence interval (CI): 0.734-0.942, P<0.001] and excellent (95% CI: 0.970-0.996, P<0.001) in prone, supine, and sitting. Intra-rater reliability was excellent (95% CI's: 0.876-1.000, P's<0.001) in all postures. There were no differences between the motor performance of exposed infants compared to the unexposed normative sample. It was feasible to assess the motor performance of infants exposed to SARS-CoV-2 via video call with good to excellent inter- and intra-rater reliabilities, making it an important approach when social distancing is needed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".