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Record W2157338832 · doi:10.1080/17461391.2010.499976

Effect of tapering period on plasma hormone concentrations, mood state, and performance of elite male cyclists

2011· article· en· W2157338832 on OpenAlexaff
Farzad Zehsaz, Mohammad Ali Azarbaijani, Negin Farhangimaleki, Peter M. Tiidus

Bibliographic record

VenueEuropean Journal of Sport Science · 2011
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsTaperingTime trialMoodMedicineTestosterone (patch)Animal scienceProfile of mood statesInternal medicinePsychologyPhysical therapyEndocrinologyHeart rateBiologyBlood pressurePsychiatry

Abstract

fetched live from OpenAlex

Abstract In this study, we investigated the effect of 1‐ and 3‐week tapering periods on concentrations of plasma testosterone (T), cortisol (C), T/C ratio, mood state, and performance in elite male cyclists. After 8 weeks of progressive training, cyclists were randomly assigned to a control group ( n =12) who continued performing intense training for a further 3 weeks, or a taper group ( n =12) who continued with a 50% reduction in training volume. Blood testosterone and cortisol concentrations were assayed and the T/C ratio calculated from analysis obtained via standard ELISA. Mood state was determined using the Profile of Mood States (POMS) questionnaire. All data were collected immediately after a 40‐km time‐trial performed before, during, and after the 8‐week training protocol and after the 1‐ and 3‐week tapering/training periods. In the taper group, 40‐km time‐trial time decreased significantly ( P <0.01) and equally for both the 1‐ and 3‐week taper periods relative to the control group. There were significant elevations in T/C ratio ( P <0.001) and reductions in cortisol concentrations and POMS scores in the taper group relative to the control groups at the end of both the 1‐ and 3‐week tapering periods. Hence, taper periods are effective in improving performance and mood state and elevating the blood T/C ratio.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.014
GPT teacher head0.240
Teacher spread0.227 · 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

Citations11
Published2011
Admission routes1
Has abstractyes

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