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Record W2906119451 · doi:10.33233/fb.v19i4.1321

Desempenho motor de recém-nascidos prematuros: Alberta Infant Motor Scale

2018· article· pt· W2906119451 on OpenAlexaboutno aff
Diana Teixeira Rebouças, Laisla Pires Dutra, Isnanda Taciara Da Silva, Jacielle Brito Alves, Daiane Porto Nery, Jéssica Matos Veiga

Bibliographic record

VenueFisioterapia Brasil · 2018
Typearticle
Languagept
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMotor activityPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Prematuros podem apresentar suscetí­veis atrasos no desenvolvimento motor, tornando-se necessário o seu acompanhamento. O objetivo deste estudo foi avaliar o desempenho motor de prematuros nascidos em municí­pio baiano, segundo a Alberta Infant Motor Scale (AIMS). Trata-se de um estudo observacional longitudinal prospectivo com caráter quantitativo. A amostra foi composta por 42 lactentes nascidos com idade gestacional < 37 semanas e idade corrigida de 40 semanas, residentes em Vitória da Conquista ou proximidades, que passaram por cuidados intensivos neonatais. Observou-se a prevalência de mães que realizaram ≤ 6 consultas pré-natal, sendo o parto cesária o mais prevalente. Houve predomí­nio de lactentes do sexo feminino, com Idade Gestacional (IG) moderada e média de Apgar de 6,3 (±1,9) no 1º e 7,9 (±1,2) no 5º minuto. Evidenciou-se que quanto maior a IG, menor é o tempo de internamento em igual proporção. Na avaliação da escala, predominou o desenvolvimento motor normal, com apresentação de desenvolvimento atí­pico na 3ª avaliação. Longos perí­odos de internamento podem repercutir negativamente no desenvolvimento neuropsicomotor do lactente, podendo apresentar atraso nas habilidades motoras futuras, sendo a AIMS eficaz no acompanhamento de prematuros.Palavras-chave: desenvolvimento infantil, prematuro, saúde pública.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.215
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.268
Teacher spread0.256 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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