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Record W3096372301 · doi:10.1002/tsm2.210

Effect of one‐week oral or inhaled salbutamol treatment with washout on repeated sprint performance in trained subjects

2020· article· en· W3096372301 on OpenAlexfundno aff
Kasper Eibye, Glenn A. Jacobson, Kasper Høtoft Bengtsen, Søren Jessen, Vibeke Backer, Jens Bangsbo, Morten Hostrup

Bibliographic record

VenueTranslational Sports Medicine · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsSalbutamolSprintWashoutMedicineAnesthesiaPhysical therapyInternal medicineAsthma

Abstract

fetched live from OpenAlex

Background Acute and chronic supratherapeutic treatment with the commonly used beta2-agonist salbutamol has the potential to enhance sprint performance and muscle strength. However, little is known about the performance effects of short-term daily permitted inhaled treatment vs oral prohibited treatment in accordance with the 2020 Prohibited List issued by the World Anti-Doping Agency (WADA). Methods Herein, we investigated the effect of twice-daily treatment with 400 μg inhaled or 4 mg oral salbutamol for 1 week on repeated sprint performance in 19 healthy well-trained men and women utilizing a randomized open-label crossover design. Before and after each treatment period, and a 12-16 hours washout to avoid an acute effect of salbutamol, subjects performed a repeated sprint test (3 × 30-second Wingate). Results Neither oral nor inhaled salbutamol enhanced peak power (oral; 3.0 W; 95% CI −6.8 to 12.8 W; and inhaled; −3.8 W; 95% CI −14.3 to 6.8 W) or mean power (oral; −2.1 W; 95% CI −4.7 to 8.9 W and inhaled; −1.6 W; 95% CI −5.6 to 8.9 W) during the repeated sprint test irrespective of gender. Conclusions These findings indicate that 1 week is insufficient for salbutamol to induce any relevant effect on repeated sprint performance in trained individuals.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.040
GPT teacher head0.268
Teacher spread0.228 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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