Concurrent validity and reliability of the Alberta Infant Motor Scale in premature infants
Bibliographic record
Abstract
OBJECTIVE: To verify the concurrent validity and interobserver reliability of the Alberta Infant Motor Scale (AIMS) in premature infants followed-up at the outpatient clinic of Instituto Fernandes Figueira, Fundação Oswaldo Cruz (IFF/Fiocruz), in Rio de Janeiro, Brazil. METHODS: A total of 88 premature infants were enrolled at the follow-up clinic at IFF/Fiocruz, between February and December of 2006. For the concurrent validity study, 46 infants were assessed at either 6 (n = 26) or 12 (n = 20) months' corrected age using the AIMS and the second edition of the Bayley Scales of Infant Development, by two different observers, and applying Pearson's correlation coefficient to analyze the results. For the reliability study, 42 infants between 0 and 18 months were assessed using the Alberta Infant Motor Scale, by two different observers and the results analyzed using the intraclass correlation coefficient. RESULTS: The concurrent validity study found a high level of correlation between the two scales (r = 0.95) and one that was statistically significant (p < 0.01) for the entire population of infants, with higher values at 12 months (r = 0.89) than at 6 months (r = 0.74). The interobserver reliability study found satisfactory intraclass correlation coefficients at all ages tested, varying from 0.76 to 0.99. CONCLUSIONS: The AIMS is a valid and reliable instrument for the evaluation of motor development in high-risk infants within the Brazilian public health system.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".