Segmental Assessment of Trunk Control in Moderate-to-Late Preterm Infants Related to Sitting Development
Bibliographic record
Abstract
(1) Background: The assessment of postural segment control in premature infants seems to be critical during the onset of upright gross motor development, especially sitting. Identifying correlations between postural segment control and the development of sitting milestones could help with promoting optimal gross motor movement. However, data on this topic in home-raised premature infants via longitudinal design are still limited. The purpose of this study was to examine relationships between postural segment control and sitting development through series assessments from the corrected age of 4 months until the early onset of independent sitting attainment. (2) Methods: 33 moderate-to-late premature infants were recruited. Their trunk segment control was assessed using the Segmental Assessment of Trunk Control (SATCo), and sitting development was examined by the Alberta Infant Motor Scale (AIMS). Relationships between SATCo and sitting scores were analysed using Spearman’s rank correlation (rs). (3) Results: significant fair-to-good correlations between segmental trunk control and sitting scales were found from 4 months (rs = 0.370–0.420, p < 0.05) to the age of independent sitting attainment (rs = 0.561–0.602, p < 0.01). (4) Conclusion: relationships between the trunk segment control and sitting ability of moderate-to-late preterm infants were increased in accordance with age.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".