MétaCan
Menu
Back to cohort
Record W2314512558 · doi:10.1139/apnm-2013-0097

Twitch mechanical properties after repeated eccentric exercise of the elbow flexors

2013· article· en· W2314512558 on OpenAlexvenueno aff
Damian Janecki, Anna Jaskólska, Jarosław Marusiak, Renata Andrzejewska, Artur Jaskólski

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber Systems
KeywordsEccentric exerciseElbowEccentricPhysical medicine and rehabilitationMedicinePhysical therapyAnatomyEngineeringStructural engineering

Abstract

fetched live from OpenAlex

The purpose of this study was to assess if the protective adaptation after eccentric exercise affects changes of twitch contractile properties of the biceps brachii muscle. Maximal isometric torque (MVC), twitch contractile properties, muscle soreness, and relaxed elbow angle (RANG) assessments were measured in 12 untrained, right-handed male volunteers (age, 23 ± 2 years; height, 182 ± 5 cm; mass, 75 ± 7 kg) before, immediately after, 48 h, and 120 h following each bout of eccentric exercise that consisted of 30 repetitions of lowering a dumbbell adjusted to 75% of each individual's maximal isometric torque of the right elbow flexors. MVC, peak twitch torque, maximal rate of twitch torque development, maximal rate of relaxation, muscle soreness, and RANG changes were significantly attenuated after the second bout of eccentric exercise when compared with the first bout. In contrast, time to twitch peak torque and half relaxation time did not change significantly after both the first and the second bout. The findings indicate that the mechanisms responsible for rapid adaptation affect some twitch mechanical properties such as peak torque, maximal rate of torque development, and maximal rate of relaxation but not time to peak torque and half relaxation time.

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 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 score0.538
Threshold uncertainty score0.393

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)0.0000.000

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 head0.015
GPT teacher head0.234
Teacher spread0.219 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueApplied Physiology Nutrition and MetabolismSame topicExercise and Physiological ResponsesFrench-language works237,207