Bayley Scales of Infant Development Screening Test-Gross Motor Subtest
Bibliographic record
Abstract
In Brief Purpose: To identify the efficacy of the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III), Screening Test–Gross Motor Subtest (GMS) in identifying infants who are accepted for early intervention services. Methods: This retrospective study included 93 infants with a neonatal intensive care experience who participated in a 6-month developmental assessment follow-up visit. All infants were examined using the BSID-III Screening Test–GMS and the Alberta Infant Motor Scale. A binary logical regression analysis was used to determine the best predictors of acceptance status in this sample. Results: The BSID-III Screening Test–GMS accounted for a significant portion of the variance in acceptance status. Conclusion: The results suggest that the BSID-III Screening Test–GMS has great applicability for transdisciplinary/interdisciplinary teams as it effectively identified children who were eligible for early intervention. The Bayley III Screening Test's Gross Motor Subtest was found to be a predictor of those children referred for early intervention services. The authors note the test can be administered in a relatively short time and by individuals from a variety of health disciplines.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".