Premature birth and motor milestones: the importance and use of adjusted age
Bibliographic record
Abstract
In early childhood, gross motor development is assessed and followed in accordance to motor milestones, which provide a timeline for when children typically acquire various movement skills. Motor milestone acquisition reflects and depends on the development of the central nervous system. However, commonly used motor milestone charts are based on the development of children born full-term. While children born premature acquire motor milestones in the same sequential nature as children born full-term, they often exhibit delays in the emergence of motor milestones. These delays are often attributed to immaturity of the central nervous system. As such, it is important when monitoring the motor development of children born preterm, that time for the system to catch up developmentally to their full-term peers is taken into account and timelines adjusted accordingly. Adjusting for age is a method to account for prematurity, by subtracting how preterm a child was born from their chronological age. It is important for parents and health professionals alike to understand how to calculate adjusted age, which supports the importance of creating and disseminating resources in the area. This infographic and accompanying summary provides information on adjusting for age for motor milestone acquisition in a premature population.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".