Reliability of Alberta Infant Motor Scale Using Recorded Video Observations Among the Preterm Infants in India: A Reliability Study
Bibliographic record
Abstract
Background: Assessment of motor function is a vital characteristic of infant development. Alberta Infant Motor scale (AIMS) is considered to be one of the tool available for screening the developmental delays, but this scale was formulated by using western samples. Every country has its own ethnic and cultural background and various differences are observed in the culture and ethnicity. Therefore, there is a need to obtain reliability for the use of AIMS in south Indian population. Purpose: To find the intra-rater and inter-rater reliability of Alberta Infant Motor Scale (AIMS) on pre-term infants using the recorded video observations in Indian population. Method: 30 preterm infants in three age groups, 0-3 months (10 infants), 4-7 months (10 infants), 8-18 months (10 infants) were recruited for this reliability study. The AIMS was administered to the preterm infants and the performance was videotaped. The performance was then rescored by the same therapist, immediately from the video and on another two consecutive months to estimate intra-rater reliability using ICC (3,1), two-way mixed effects model. For reporting inter-rater reliability, AIMS was scored by three different raters, using ICC (2,k) two-way random effects model and by two other therapists to examine the inter and intra-rater reliability. Results: The two-way mixed effects model for intra-rater reliability of AIMS, ICC (3,1) = 0.99 and for reporting inter-rater reliability of AIMS by two-way random effects model, ICC (2,k) = 0.96. Conclusion: AIMS has excellent intra and inter-rater reliability using recorded video observations among the preterm infants in India
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".