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Evaluation of Pre-Participation Screening and Risk Assessment in Masters Athletes in British Columbia

2016· article· en· W2498893224 on OpenAlexaffabout
B Morrison, S. Isserow, James McKinney, Hamed Nazarri, Daniel Lithwick, Brett Heilbron, Jack Taunton, Darren E. R. Warburton

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsAthletesMedicinePhysical therapyDiseasePopulationMyocardial infarctionFramingham Risk ScoreSudden cardiac deathGerontologyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: There has been increased participation in recreational and competitive athletics by middle-aged individuals. Although routine physical activity is associated with health benefits, vigorous physical activity can transiently increase the risk of myocardial infarction or sudden cardiac death in those with unidentified cardiovascular disease. Pre-participation screening (PPS) may be a way to detect underlying disease and mitigate the risk. PURPOSE: To evaluate the prevalence of cardiovascular disease (CVD) and risk factors amongst British Columbia Masters athletes (>35 years). METHODS: Masters athletes from a wide range of sporting activities living in British Columbia, Canada were invited to participate. Athletes were eligible if they participated in sport >3 times per week at a moderate to vigorous intensity. RESULTS: 185 athletes were screened; 60%, 29% and 11% had low, intermediate, and high Framingham Scores (FRS), respectively. A new cardiovascular abnormality was discovered in 14% of the participants with 38 cases pending investigations. CONCLUSION: Given the significant amount of CVD detected in this population, PPS may be warranted amongst Masters athletes. Further study is required in order to determine whether this strategy reduces adverse cardiovascular events and is cost-effective.

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)0.017
metaresearch head score (Gemma)0.002
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 score0.424
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.317
Teacher spread0.298 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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