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Record W2909531195

Spécificités électrocardiographiques et échocardiographiques des jeunes athlètes métis réunionnais

2018· article· en· W2909531195 on OpenAlexaboutno aff
Romain Perrin

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Effects of Exercise
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAthletesGynecologyPhysical therapy
DOInot available

Abstract

fetched live from OpenAlex

Background: electrocardiographic (ECG) and echocardiographic (ETT) characteristics of Afro-Caribbean athletes changed recommendations in sports cardiology. No specific data has been provided for mixed-race athletes so far. Aims: To compare ECG and ETT characteristics of adolescent mixed-race (M) athletes with those of Caucasian (C) and African (A) athletes. Material and Methods: between 12/2015 and 08/2017, 100 young athletes born in Reunion Island (70 M, 18 C, 12 A) were included in our study. ECG and ETT were performed during a pre-participation screening before competitive sporting activity. Results: M athletes showed no ECG specificity. The “Afro-Caribbean athletes’ repolarization” was found in 5.7% of M athletes. M and A athletes demonstrated a greater wall thickening induced by exercise compared to C athletes (IVSd indexed at 5.2 ± 0.6mm/m2 and 5.3 ± 0.6mm/m2 [p M/A = 0.363] versus 4.8 ± 0.6mm/m2 respectively [p M/C = 0.011, p A/C = 0.024]). M and C athletes featured larger left-ventricular cavity size compared to A athletes (LVIDd indexed at 28.8 ± 2.9mm/m2 and 28.9 ± 2.4mm/m2 [p M/C = 0.887] versus 26.4 ± 2.5mm/m2 respectively [p M/A = 0.011, p C/A = 0.013]). Conclusion: morphologic left ventricular (LV) remodeling in mixed-race adolescent athletes is characterized by a greater LV cavity enlargement compared to A athletes and a significant increase in LV wall thickness compared to C athletes. ”Afro- Caribbean repolarization” is occasionally seen among these athletes.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

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