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Record W2603860813 · doi:10.1002/mus.25560

Effect of resistance training with vibration and compression on the formation of muscle and bone

2017· article· en· W2603860813 on OpenAlexaff
Christoph Zinner, Bettina Baeßler, Kilian Weiss, Jasmine Ruf, Guido Michels, Hans‐Christer Holmberg, Billy Sperlich

Bibliographic record

VenueMuscle & Nerve · 2017
Typearticle
Languageen
FieldMedicine
TopicEffects of Vibration on Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBone massAnatomyChemistryMedicineInternal medicineOsteoporosis

Abstract

fetched live from OpenAlex

INTRODUCTION: In this study we investigated the effects of resistance training with vibration in combination with leg compression to restrict blood flow on strength, muscle oxygenation, muscle mass, and bone formation. METHODS: Twelve participants were tested before and after 12 weeks of resistance training with application of vibration (VIBRA; 1-2 mm, 30 Hz) to both legs and compression (∼35 mm Hg, VIBRA+COMP) to only 1 leg. RESULTS: VIBRA+COMP and VIBRA improved 1 repetition maximum (1-RM), increased the number of repetitions preceding muscle exhaustion, enhanced cortical bone mass, and lowered the mass and fat fraction in the thigh, with no changes in total muscle mass. The mass of cancellous bone decreased to a similar extent after VIBRA and VIBRA+COMP. DISCUSSION: Resistance training with VIBRA+COMP and VIBRA improved 1-RM, increased the number of repetitions preceding muscular exhaustion, and enhanced formation of cortical bone, with no alteration of muscle mass. Muscle Nerve 56: 1137-1142, 2017.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.305
Teacher spread0.277 · 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 designBench or experimental
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

Citations9
Published2017
Admission routes1
Has abstractyes

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