Bibliographic record
Abstract
“How could I apply this information?” The authors suggest adaptations to the Gross Motor Function Measure (GMFM-88 and GMFM-66) to assess motor development in children younger than 2 years. In the literature and in practice, these instruments are used to measure change in motor development over time in children with cerebral palsy, but lack application to the infant population. In comparison, the Alberta Infant Motor Scale (AIMS), the Bayley Scales of Infant Development II (BSID II) (now III), and the Infant Motor Profile are used as scales for detecting infants who are “at risk” as well as to determine their developmental age. The suggestions for adaptation meet a clinical need for assessing younger infants who present with signs of cerebral palsy. Crediting skills that are no longer observed because the infant is capable of higher-level motor function is a valid suggestion, just as the AIMS and the BSID-II credit previous items of function below the baseline. This adaptation is practical since the goal of assessment and intervention in these children is to capture typical performance, and the “loss” of lower-level skills is expected in the trajectory of typical development. “What should I be mindful about in applying this information?” This study would be strengthened by using the percentiles or scaled scores of the AIMS and BSID III for comparison with the Gross Motor Function Measure scores, since the raw scores are the least reliable indicators for either test. The suggestion for arbitrary assignment of a score for a dimension negates the foundation of standardized testing and may not result in accurate results when used in a larger, more variable population. Stronger clinical relevance would be achieved by using the AIMS and BSID III, which are designed specifically for the infant population with valid and reliable results. However, the authors increase awareness of the lack of specific testing in younger infants at risk for cerebral palsy. The bias of the adapted tool should also be considered, since removing items and scoring unobserved items are likely to skew the test toward improvement, regardless of patient performance. Amber Richards, MPT, PCS Children's Hospital Los Angeles Los Angeles, California Linda Fetters, PT, PhD, FAPTA University of Southern California Los Angeles
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.047 | 0.065 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".