Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
Bibliographic record
Abstract
Background: Early interventions in high-risk children seek to improve prognosis, minimize developmental delays, and prevent functional deterioration. The objective of this study was to evaluate the level of agreement between the face-to-face assessment and tele-assessment of neuromotor development in high-risk children between 0 and 18 months of age. Methods: Forty-five children at high risk of developmental delays were included in this study (33% female, mean gestational age of 35.31 ± 4.03 weeks). The patients were included in a face-to-face and a tele-assessment using the Alberta Infant Motor Scale (AIMS) and the level of motor evolution (Niveaux d’Évolution Motrice, NEM) assessments. Results: The analysis showed excellent interrater reliability (ρ ≥ 0.99) for the AIMS. The NEM assessment showed almost perfect reliability (kappa ≥ 0.81) for most items. Seven of them showed substantial reliability (kappa = 0.61–0.80), one moderate reliability (kappa = 0.568), and one fair reliability (kappa = 0.338). Conclusions: This study reveals an excellent/substantial interrater reliability for most of the items assessed. The results are promising to increase the accessibility to a clinical diagnosis and a rehabilitation approach to minimize the development of neuromotor delays in children at high risk.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".