Evaluation of the InterRAI Early Years for Degree of Preterm Birth and Gross Motor Delay
Bibliographic record
Abstract
Background: The interRAI 0-3 Early Years was recently developed to support intervention efforts based on the needs of young children and their families. One aspect of child development assessed by the Early Years instrument are motor skills, which are integral for the maturity of cognition, language, social-emotional and other developmental outcomes. Gross motor development, however, is negatively impacted by pre-term birth and low birth weight. For the purpose of known-groups validation, an at-risk sample of preterm children using the interRAI 0-3 Early Years was included to examine correlates of preterm risk and the degree of gross motor delay. Methods: = 591) from 17 health agencies in Ontario, Canada. Data were collected as part of a pilot study using the full interRAI 0-3 Early Years assessment. Correlational analyses were used to determine relationships between prenatal risk and preterm birth and bivariate analyses examined successful and failed performance of at-risk children on gross motor items. A Kruskal-Wallis test was used to determine the mean difference in gross motor scores for children born at various weeks gestation. Results: Correlational analysis indicated that prenatal and perinatal factors such as maternal nicotine use during pregnancy did not have significant influence over gross motor achievement for the full sample, however, gross motor scores were lower for children born pre-term or low birth weight based on bivariate analysis. Gross motor scores decreased from 40 weeks' gestation (mean rank = 310.77), to moderate to late preterm (mean rank = 258.96), and to very preterm (mean rank = 234.54), however extremely preterm (mean rank = 236.28) performed comparably to very preterm. Interpretation: the interRAI 0-3 Early Years gross motor domain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".