General movements assessment and Alberta Infant Motor Scale in neurodevelopmental outcome of preterm infants
Bibliographic record
Abstract
AIM: We aimed to compare the General Movement Assessment (GMA) and the Alberta Infant Motor Scale (AIMS) in preterm infants for the prediction of cerebral palsy (CP) and neurodevelopmental delay (NDD). Additionally, we aimed to evaluate the diagnostic compatibility of the General Movement Optimality Score (GMOS), the Motor Optimality Score (MOS), and AIMS for detecting CP and NDD. METHOD: Seventy-five preterm infants with gestational age (GA) 24-37 weeks were enrolled. Group 1 was composed of infants with 24-28 GA (n = 22); groups 2 and 3 consisted of infants with 29-32 GA weeks (n = 23) and 33-37 GA (n = 30) weeks, respectively. The infants were assessed during the writhing period, the fidgety period, and at 6-12 months of corrected age with GMOS, MOS, and AIMS, respectively. RESULTS: In the writhing period, a cramped-synchronized pattern was observed in 17 (22%) infants, whereas a poor repertoire pattern was observed in 34 (45%) infants. In the fidgety period of the 63 infants, 29 (46%) presented with fidgety movements absent. The MOS and AIMS scores of the infants in group 1 were significantly lower than the other groups, which were statistically significant (p = 0.004, p˂0.001). High and positive compatibility (Kappa coefficient: 0.709; p = 0.001) was found between AIMS and GMOS scores and between AIMS and MOS scores (Kappa coefficient: 0.804; p < 0.001). In all groups, a statistically significant association was found between total GMOS scores (p = 0.003) and the presence of fidgety movements (p = 0.003). GMOS, MOS, and AIMS were found to be associated with CP and NDD (p < 0.001). CONCLUSION: GMA is an important tool for the prediction of CP and NDD. The combined use of GMOS, MOS, and AIMS may guide the clinical practice for the valid and reliable diagnosis of CP and NDD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".