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Record W3024524511

[STRENGTH TRAINING IN CHILDREN].

2019· article· en· W3024524511 on OpenAlexaff
Bareket Falk, Raffy Dotan

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Training Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsMedicineOverweightStrength trainingTraining (meteorology)Incidence (geometry)Resistance trainingResistance (ecology)Muscle strengthObesityPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: The approach to strength training in children still suffers from reliance on misguided notions claiming them as being ineffective and more conducive to injuries than in adults. Not only are those notions wrong, but children's response to strength training is actually rather similar to that in adults, although they don't gain as much muscle mass. Under proper guidance and supervision, the resistance-training-related incidence of injury is not higher in children than in adults. Strength training has other benefits for children, beyond the actual strength improvement. It can help reduce the risk of activity-related injuries, in general, and especially in other sports. In overweight youth, it can also improve the metabolic profile and help in the management of conditions such as diabetes, contribute to the prevention or attenuation of obesity, and may even improve mental health.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0600.012

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 head0.080
GPT teacher head0.382
Teacher spread0.302 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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