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Record W4309785779 · doi:10.3390/nu14224921

High Doses of Caffeine Increase Muscle Strength and Calcium Release in the Plasma of Recreationally Trained Men

2022· article· en· W4309785779 on OpenAlexaff
Luís Henrique Boiko Ferreira, Scott C. Forbes, Marcelo P. Barros, André de Camargo Smolarek, Alysson Enes, Antônio Herbert Lancha, Gabriel Loureiro Martins, Tácito Pessoa de Souza

Bibliographic record

VenueNutrients · 2022
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsBrandon University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCaffeineBench pressSquatCrossover studyCalciumPlaceboChemistryAnimal scienceEndocrinologyMedicineInternal medicineResistance trainingPhysical therapyBiology

Abstract

fetched live from OpenAlex

The effects of acute caffeine supplementation on muscular strength remain unclear. We examined the effects of two different doses of caffeine on muscle strength and calcium in plasma compared to placebo using a crossover, randomized, double-blind, placebo-controlled design. Twenty-one (n = 21) recreationally resistance-trained participants were randomly assigned into three experimental conditions: 6 mg·kg bw−1 of caffeine (CF6); 8 mg·kg bw−1 of caffeine (CF8); or placebo (PLA), with a 7-day washout period between conditions. Muscular strength assessments were made for both upper (bench press) and lower body muscles (squat and deadlift). Calcium release in plasma was measured on five different occasions. Bench press (CF8: 100.1 ± 1.9 kg; PLA: 94.2 ± 2.5 kg), deadlift (CF8: 132.8 ± 3.5 kg; PLA: 120.7 ± 5.7 kg), and squat (CF8: 130.1 ± 4.9 kg; PLA 119.4 ± 5.4 kg) strength were all significantly (p < 0.001) improved in CF8 compared to PLA. Calcium release in plasma was significantly increased in CF8, whereas no changes were observed in CF6 or PLA. Overall, 8 mg·kg bw−1 of caffeine appears to be an effective dose to optimize upper and lower body muscular strength and calcium release in recreationally trained participants.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.431
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.025
GPT teacher head0.293
Teacher spread0.269 · 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

Citations36
Published2022
Admission routes1
Has abstractyes

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