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Record W4281788094 · doi:10.1177/1357633x221102250

Telemonitoring of motor skills using the Alberta Infant Motor Scale for at-risk infants in the first year of life

2022· article· en· W4281788094 on OpenAlexaboutno aff
Camila Resende Gâmbaro Lima, Bruna Nayara Verdério, Raissa Wanderley Ferraz de Abreu, Beatriz Helena Brugnaro, Adriana Neves dos Santos, Mariana Martins dos Santos, Nelci Adriana Cicuto Ferreira Rocha

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2022
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsScale (ratio)Motor skillMedicinePsychologyPhysical medicine and rehabilitationDevelopmental psychologyGeography

Abstract

fetched live from OpenAlex

INTRODUCTION: Remote assessment creates opportunities for monitoring child development at home. Determining the possible barriers to and facilitators of the quality of telemonitoring motor skills allows for safe and effective practices. We aimed to: (1) determine the quality, barriers and facilitators of Alberta Infant Motor Scale (AIMS) home videos made by mothers; (2) verify interrater reliability; (3) determine the association between contextual factors and the quality of assessments. METHODS: Thirty infants at biological risk aged between three and ten months, of both sexes, and their mothers were included. Assessments were based on asynchronous home videos, where motor skills were evaluated by mothers at home according to AIMS guidelines. The following were analyzed: video quality; stimulus quality; camera position; and physical environment. The video characteristics were analyzed descriptively. The intraclass correlation coefficient was used to calculate interrater reliability and the regression model to determine the influence of contextual factors on the outcome variables. Significance was set at 5%. RESULTS: = 0.980). The contextual factors had no relation with assessment quality. DISCUSSION: Assessments conducted remotely by the mothers showed high video quality and interrater reliability, and represent a promising assessment tool for telemedicine in at-risk infants in the first year of life.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.153
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
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 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

Citations13
Published2022
Admission routes1
Has abstractyes

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