'Interrater reliability' of Alberta Infant Motor Scale in term and preterm born infants between 10 to 16 months age from Talca province - Chile
Bibliographic record
Abstract
Purpose: To examine the ‘interrater reliability’ of the Alberta Infant Motor Scale (AIMS) in term and preterm born infants between 10 to 16 months age from Talca province, Maule Region - Chile. Subjects: 115 infants between 10 to 16 months age were incorporated to the study; 95 term born infants were attended in the local Health Centre in Talca City, and 20 preterm infants belonged to the Premature Infants Follow-Up Programme of Talca Regional Hospital. Methods: The motor behaviour of each infant was recorded and later it was assessed by two trained assessors using AIMS. It was obtained the total AIMS’ score and also from prone, supine, seated, and stand subscales. For ‘interrater reliability’ analysis it was used the Intraclass Coefficient of Correlation (ICC), the Standard Error of Measurement (SEM) and 95% limits of agreement. Results: The obtained ICC for the total scores AIMS were major than 0.94 (p<0.0002) for term and preterm born infants. The SEM of total scores was less than 3.1 points, higher than what was found in other similar studies. The 95% limits of agreement were +5.3 to -4.1 points and +7.7 to – 3.9 points in term and preterm born, respectively, revealing ‘interrater agreement’. Conclusion: The AIMS showed adequate ‘interrater reliable’ levels when was applied in Chilean term and preterm born from 10 to 16 month’s 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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".