A comparison of Alberta’s Infant Motor Scale and Brazelton Neonatal Behavior Assessment Scale
Bibliographic record
Abstract
Abstract Background and aims The Neonatal Behavior Assessment Scale (NBAS) was introduced by Brazelton is a useful tool to assess neurodevelopment in newborn in research setting. However, it is complex, time-consuming and requires specialized training for administration and is therefore difficult tool to use in routine clinical settings. The Alberta Infant Motor Scale (AIMS) is a simple tool to assess of motor maturation of a child up to 18 months and can be administered with minimal training for the health care professionals. The study goal of this study was to evaluate the reliability and validity of the AIMS in comparison with the NBAS in assessing motor maturity at birth. Methods We administered the AIMS and NBAS to a total of 66 newborn babies delivered in three obstetric units at the Colombo North Teaching Hospital in Ragama, Sri Lanka. The subjects were selected from a sample of 545 newborn babies in an ongoing birth-cohort study evaluating effects of prenatal exposure to biomass smoke and infant neurodevelopment. Trained research assistants administered first the NBAS, followed by the AIMS one-hour later. Univariate and bivariate statistics were used to compare the two scales. Results Irrespective of maturity, sex, birth weight or socio-demographic characteristics, all babies had scored on the 75th percentile in the AIMS. In the NBAS, there was a significant variation in the Brazelton motor score scaled to 100. Low birth weight babies showed a narrower variation in the NABS score. None of the scales indicated a motor deficit in any of the children. Conclusion NBAS identifies subtle differences in motor maturity of full term babies that the AIMS fails to detect
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".