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Record W2995868636 · doi:10.3390/ijerph16245029

Development of Physical Performance Tasks during Rapid Growth in Brazilian Children: The Cariri Healthy Growth Study

2019· article· en· W2995868636 on OpenAlexaff
Simonete Silva, Hudday Mendes, Duarte Freitas, António Prista, Go Tani, Peter T. Katzmarzyk, Adam Baxter‐Jones, Alcibíades Bustamante, José Maia

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2019
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Saskatchewan
FundersKU Leuven
KeywordsMedicineDemographyGrowth velocityAnnual growth %BiologyInternal medicine

Abstract

fetched live from OpenAlex

Growth and physical performance scores were studied around three years of attainment of peak height velocity (PHV). We aimed to estimate the age at peak velocity, or at peak rate, in physical performance tasks, and sex-differences when aligned by biological age. A total of 131 boys and 123 girls, 8 to 14 years of age were recruited from the Cariri region of Brazil. A mixed longitudinal design was used with four overlapping age cohorts: 8, 10, 12, and 14 years, followed for three years, with measurements performed at 6 month intervals. Height, 12 min run (12mR), handgrip strength (HG), standing long jump (SLJ), and shuttle run (SR) velocities were estimated using a non-smooth mathematical procedure. Age at PHV was 13.4 ± 1.6 years in boys compared with 12.2 ± 2.3 years in girls. Maximal velocity in SLJ was attained 6 and 3 months prior to PHV in boys and girls, respectively. For HG, peaks were attained 9 months after PHV in boys and 15 months after PHV in girls. Maximal velocity in 12mR was attained 6 months before PHV in boys and at PHV in girls, whereas a peak in SR occurred 12 months after PHV in both sexes. In conclusion, dynamic changes in physical performance relative to PHV appear similar in both sexes, although sex differences were evident in some motor tests.

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)0.002
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 score0.104
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.346
Teacher spread0.315 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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