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Blood lactate concentrations following maximal incremental test in male runners with different ages

2018· article· en· W3007746636 on OpenAlexfundno aff
Cecília Segabinazi Peserico, Danilo Fernandes da Silva, Ana Claudia Pelissari Kravchychyn, Júlio César Camargo Alves, Fabiana Andrade Machado

Bibliographic record

VenueRevista Brasileira de Educação Física e Esporte · 2018
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Ottawa
KeywordsBlood lactateTreadmillMedicineAnimal scienceIncremental exerciseInternal medicineHeart rateBiologyBlood pressure

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the effect of age on peak blood lactate concentration following a maximal incremental treadmill test in male recreational runners. Seventy runners from four age groups, ≤25 years; 26-35 years; 36-45 years; >45 years, performed an incremental treadmill test starting at 8 km·h-1, and increasing by 1 km·h-1 every three minutes until volitional exhaustion. Blood samples were collected at baseline and at the zero, third, fifth and seventh minutes after test to determine lactate concentrations. Peak lactate concentration (LApeak) was defined for each participant as the highest value among the four samples. The lactate concentrations were influenced by the participants’ age (r = -0.47), with LApeak of the younger runners (10.8 ± 2.6 mmol·L) being higher than the values for the older age categories (8.1 ± 3.1, 7.0 ± 1.1 and 6.9 ± 2.8 mmol·L for those 26-35, 36-45 and >45 years, respectively). The LApeak occurred more frequently at the third and fifth minute after the initiation of the test. In conclusion, the lactate concentrations were higher in the younger group (< 25) and reached peak more frequently at the third and fifth minute after the incremental test.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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