45: The Movement Assessment of Infants (MAI) and the Alberta Infant Motor Scale (AIMS) at Four Months: Do they Accurately Predict 18-Months Outcome of Extremely Preterm Infants?
Bibliographic record
Abstract
The MAI and AIMS have been widely used to assess infant neuromotor status. Their predictive validity with the first editions of the Bayley has been modest. To examine the predictive validity of the MAI and AIMS at four months corrected age (CA) in determining neurodevelopmental outcome at 18 months (CA) using the Bayley Scales of Infant and Toddler Development 3rd edition (BSITD-3). This retrospective cohort study included all surviving infants <29 weeks gestation born at or admitted at a tertiary care university center in 2009 to 2011 and followed until 18 months. All were assessed at four months with the MAI and AIMS by a physiotherapist and at 18 months with the BSITD-3 by a psychologist, blinded to previous test scores, and for cerebral palsy (CP) by a physician. Abnormal results were: MAI ≥14, AIMS ≤5th percentile, Bayley score ≤85 or CP. Severe outcome was: Bayley ≤70, severe CP, bilateral blindness, severe bilateral neurosensory hearing loss. Characteristics for the 133 children were: mean (± SD) gestational age 26.2±1.4 weeks, birth weight 891±202 g, 55% boys, 25% multiples, 91% prenatal steroids, 95% inborn, 94% appropriate weight for age, 65% cesarean section; and for the mothers: 30.7±5.0 years; 68% caucasian, 62% college or university education. Risk factors were: 38% bronchopulmonary dysplasia with home oxygen, 11% severe retinopathy, 7% severe cerebral anomaly, 35% sepsis, 20% patent ductus with intervention, 10% necrotizing enterocolitis. Infants with normal MAI (≤13), compared to those with abnormal MAI (≥14), had higher cognitive, language and motor Bayley scores (all Anovas <.005); less cognitive or language score ≤85 and less severe global impairments (all χ2 <0.05). Motor score ≤85 and CP were not different. Classification according to AIMS normal or abnormal, to both tests normal, one test out of two abnormal or both tests abnormal did not improve prediction. In predicting severe outcomes the MAI had: 95% sensitivity, 47% specificity, 98% negative predictive value and 23% positive predictive value. Preterms with MAI scores ≤13 at four months have better outcomes than those with higher scores. However, both the MAI and AIMS do not strongly predict neurodevelopment status at 18 months.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".