MétaCan
Menu
Back to cohort

Intraclass reliability of the Alberta Infant Motor Scale in the Brazilian version

2013· article· en· W2000106684 on OpenAlexaboutno aff
Larissa Paiva Silva, Polyana Candeia Maia, Márcia Maria Bragança Lopes, Maria Vera Lúcia Moreira Leitão Cardoso

Bibliographic record

VenueRevista da Escola de Enfermagem da USP · 2013
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsIntraclass correlationPercentileReliability (semiconductor)Gestational ageSittingSupine positionMedicineGross motor skillDemographyScale (ratio)Physical therapyPsychologyGerontologyPediatricsGeographyStatisticsMotor skillPsychometricsPregnancyCartographyClinical psychologySurgeryMathematics

Abstract

fetched live from OpenAlex

This study had as its objective to analyze the intraclass reliability of the Alberta Infant Motor Scale (AIMS), in the Brazilian version, in preterm and term infants. It was a methodological study, conducted from November 2009 to April 2010, with 50 children receiving care in two public institutions in Fortaleza, Ceará, Brazil. Children were grouped according to gestational age as preterm and term, and evaluated by three evaluators in the communication laboratory of a public institution or at home. The intraclass correlation indices for the categories prone, supine, sitting and standing ranged from 0.553 to 0.952; most remained above 0.800, except for the standing category of the third evaluator, in which the index was 0.553. As for the total score and percentile, rates ranged from 0.843 to 0.954. The scale proved to be a reliable instrument for assessing gross motor performance of Brazilian children, particularly in Ceará, regardless of gestational age at birth.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueRevista da Escola de Enfermagem da USPSame topicInfant Development and Preterm CareFrench-language works237,207