MétaCan
Menu
Back to cohort
Record W3158347989 · doi:10.1136/bmjsem-2021-001071

Effects of beta-blockers on archery performance, body sway and aiming behaviour

2021· article· en· W3158347989 on OpenAlexfundno aff
Emin Ergen, Tahir Hazır, Mehmet Mesut Çelebi, Ayşe KİN İŞLER, Serdar Arıtan, Volkan Daghan Yaylioglu, Rüştü Güner, Caner Açıkada, Alpan Cinemre

Bibliographic record

VenueBMJ Open Sport & Exercise Medicine · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanics and Biomechanics Studies
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsBETA (programming language)Physical medicine and rehabilitationMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aimed to determine the effect of selective (bisoprolol-5 mg) and non-selective (propranolol-40 mg) beta-blockers on archery performance, body sway and aiming behaviour. METHODS: Fifteen male archers participated in a randomised, double-blind, placebo-controlled, cross-over study and competed four times (control, placebo, selective (bisoprolol) and non-selective (propranolol) beta-blocker trials). Mechanical data related to the changes in the centre of pressure during body sway and aim point fluctuation and when shooting was collected. During the shots, heart rate was recorded continuously. RESULTS: Results indicated that, in beta-blocker trials, although shooting heart rates were lowered by 12.8% and 8.6%, respectively, for bisoprolol and propranolol, no positive effect of beta-blockers was observed on shooting scores. Also, the use of beta-blockers did not affect shooting behaviour and body sway. CONCLUSION: The use of either selective or non-selective single dose beta-blockers had no positive effect on shooting performance in archery during simulated match conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.020
GPT teacher head0.279
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Open Sport & Exercise MedicineSame topicMechanics and Biomechanics StudiesFrench-language works237,207