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Record W4394446322 · doi:10.6084/m9.figshare.20015523

Parental intervention improves motor development in infants at risk: case series

2022· dataset· en· W4394446322 on OpenAlexaboutno aff
Fabiane Elpídio de Sá, Natália Paz Nunes, Edna Jéssica Lima Gondim, Ana Karine Fontenele de Almeida, Ana Júlia Couto de Alencar, Kátia Virgínia Viana Cardoso

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsSeries (stratigraphy)Intervention (counseling)PsychologyPhysical medicine and rehabilitationMedicineBiologyPsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Early intervention based on parental activities promotes cognitive, physical, social, and emotional development, which are determinants for the child’s health. However, studies about early intervention with parental education are scarce. The objective of this study was to analyze the effect of parental intervention in the motor development of infants. This is a case series, longitudinal, and interventional study, with 100 infants at risk, aged 0-18 months. Motor development of infants was assessed by the Alberta Infant Motor Scale, and the parents received information about positioning and exercises depending on the child’s motor score. Risk factors were not related to infants’ motor development. However, these risk factors were related to gestational age, which was related to motor development. After parental early intervention, the sample frequency increased from 45% to 69% in the group of children with normal motor development. Frequency was reduced from 55% to 31% in the group with delayed development. Prenatal and/or perinatal risk factors can cause prematurity, and consequently, delays in children’s motor development. For these infants, early intervention protocols with parental education are effective to stimulate a normal motor development of children at risk in follow-up in outpatient clinics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.702
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.7020.000

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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations3
Published2022
Admission routes1
Has abstractyes

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