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Record W2329951897 · doi:10.1097/pep.0b013e318288d318

Commentary on “GMFM in Infancy

2013· article· en· W2329951897 on OpenAlexaboutno aff
Amber Richards, Linda Fetters

Bibliographic record

VenuePediatric Physical Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsCerebral palsyBayley Scales of Infant DevelopmentRaw scorePercentileMotor skillGross motor skillPopulationPsychologyChild developmentDevelopmental psychologyPercentile rankPhysical medicine and rehabilitationPediatricsMedicinePsychomotor learningRaw dataComputer scienceCognitionStatisticsEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

“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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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