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Record W2479639709 · doi:10.1080/02640414.2016.1209306

A meta-analysis of maturation-related variation in adolescent boy athletes’ adaptations to short-term resistance training

2016· review· en· W2479639709 on OpenAlexaff
Jason Moran, Gavin Sandercock, Rodrigo Ramírez‐Campillo, César Meylan, Jay Collison, Dave A. Parry

Bibliographic record

VenueJournal of Sports Sciences · 2016
Typereview
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsCanadian Sport Centre Pacific
FundersUniversity of Essex
KeywordsAthletesResistance trainingMeta-analysisTraining (meteorology)Random effects modelAnalysis of varianceStrength trainingMedicinePhysical therapyDemographyPsychologyInternal medicinePhysics

Abstract

fetched live from OpenAlex

This meta-analysis investigated the maturation-related pattern of adaptations to resistance training in boy athletes. We included studies examining the effects of 4-16-week resistance training programmes in healthy boy athletes aged 10-18 years. Pooled estimates of effect size for change in strength across all studies (n = 19) were calculated using the inverse-variance random effects model for meta-analyses. Estimates were also calculated for groups based on likely biological maturity status ("before", "during" and "after" peak height velocity). Using the standardised mean difference, resistance training increased strength across all groups (effect size = 0.98, [CI: 0.70-1.27]). Strength gains were larger during (1.11 [0.67-1.54]) and after (1.01 [0.56-1.46]) peak height velocity than before (0.5 [-0.06-1.07]). Adaptations to resistance training are greater in adolescent boys during or after peak height velocity. These findings should help coaches to optimise the timing of training programmes that are designed to improve strength in boy athletes.

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)0.008
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.030
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.249
GPT teacher head0.398
Teacher spread0.148 · 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 designMeta-analysis
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

Citations149
Published2016
Admission routes1
Has abstractyes

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