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Record W2346555487 · doi:10.1123/pes.2015-0265

Influence of Exercise and Training on Critical Stages of Bone Growth and Development

2016· review· en· W2346555487 on OpenAlexaff
Panagiota Klentrou

Bibliographic record

VenuePediatric Exercise Science · 2016
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsBrock University
Fundersnot available
KeywordsOsteoporosisBone developmentMedicineBone growthAffect (linguistics)Bone massStressorBone healthPhysical therapyPhysiologyPsychologyEndocrinologyClinical psychologyBone mineral

Abstract

fetched live from OpenAlex

Although osteoporosis is considered a geriatric disease, factors affecting bone strength are most influential during child growth and development. This article reviews what is known and still unclear in terms of bone growth, development and adaptation relative to physical activity before and during puberty. Bone is responsive to certain exercise protocols early in puberty and less so in postpubertal years, where bone strength, rather than bone mass, being the outcome of interest. Mechanical loading and high impact exercise promote bone strength. Intense training before and during puberty, however, may negatively affect bone development. Future research should focus on increasing our mechanistic understanding of the manner by which diverse physical stressors alter the integrity of bone. Longitudinal studies that examine the extent to which muscle and bone are comodulated by growth in children are also recommended.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.386
Teacher spread0.330 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations27
Published2016
Admission routes1
Has abstractyes

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