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A dopamine/noradrenaline reuptake inhibitor improves performance in the heat, but only at the maximum therapeutic dose

2012· article· en· W2138867714 on OpenAlexfundno aff
Bart Roelands, Phillip Watson, Philip Cordery, Sanne Decoster, E. Debaste, Ron Maughan, Romain Meeusen

Bibliographic record

VenueScandinavian Journal of Medicine and Science in Sports · 2012
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsBupropionPlaceboEveningRating of perceived exertionMedicineMorningPerceived exertionAntidepressantAnesthesiaHeart rateInternal medicineBlood pressureSmoking cessation

Abstract

fetched live from OpenAlex

A maximal dose of bupropion has enabled subjects to maintain a higher power output than reported during the placebo session in the heat. Because this drug is taken in different doses it is important to know if there is a dose-response relationship with regard to exercise at high ambient temperature. Ten well-trained male cyclists ingested placebo (pla; 200 mg) or bupropion (50%, 75%, 100% of maximal dose: bup50: 150 mg; bup75: 225 mg; bup100: 300 mg) the evening before and morning of the experimental trial. Trials were conducted in 30 °C (humidity 48%). Subjects cycled for 60 min at 55% W (max) , immediately followed by a time trial to measure performance. Bup100 improved performance (pla: 33'42" ± 2'06"; bup100: 32'06" ± 1'54"; P = 0.035). Bupropion increased core temperature at the end of exercise, while heart rate was higher only in the bup100 trial (P < 0.05). No changes in rating of perceived exertion (RPE) or thermal sensation were found. Lower doses of bupropion were not ergogenic, indicating there was no dose-response effect. Interestingly, despite an increase in core temperature and improved performance in the maximal dose, there was no change in RPE and thermal sensation, suggesting an altered motivation or drive to continue exercise.

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)0.004
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 score0.223
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.311
Teacher spread0.275 · 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

Citations42
Published2012
Admission routes1
Has abstractyes

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