AIMS baby movement scale application in high-risk infants early intervention analysis.
Bibliographic record
Abstract
OBJECTIVE: We investigated the application of Alberta Infant Motor Scale (AIMS) in screening motor development delay in the follow-up of high-risk infants who were discharged from NICU, to explain the state of infants' motor development and propose early individualized intervention. PATIENTS AND METHODS: The study design was a randomized, single-blind trial by selecting patients between April 2015 and November 2015 in our hospital, children nerve recovery branch clinics and 77 cases of high-risk infants. We randomly divided the patients into observation group (39 cases) and control group (38 cases). To evaluate the application with AIMS, observation group was based on evaluation results for the first time to give rehabilitation training plan making, early intervention, control group according to the growth and development milestone in order to guide parents to take family training interval of 3 months. RESULTS: While comparing the two groups of high-risk infants before the intervention, the months of age, gender, risk factors, it was found that the AIMS scores, each position AIMS scores did not show a significant difference in percentile (p>0.05). There was also no significant difference between two groups in the seat and stand AIMS scores before and after intervention (p>0.05). However, the comparison of two groups of high-risk infants after intervention in comparison showed that the observation group supine AIMS scores and AIMS scores were significantly higher than the control group (p<0.05). Prone position AIMS scores observation group was also significantly higher than that of the control group (p<0.01). The corresponding percentile for two groups after the intervention of AIMS scores was less than 10% of cases, which was significantly lower in the observation group (p<0.01). CONCLUSIONS: AIMS can predict the development delay in high-risk infants, for improving the early hypernymic diagnosis and intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".