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Sport Participation In Children With Congenital Heart Disease

2024· article· en· W4402556370 on OpenAlexaff
Astrid M. De Souza, Pearl Waraich, Cindy Sha, Martin Hosking, James E. Potts, Kathryn Armstrong

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2024
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsHeart diseaseMedicinePhysical medicine and rehabilitationPhysical therapyPediatricsCardiology

Abstract

fetched live from OpenAlex

PURPOSE: Participation in sport benefits the growth and development of children and can affect activity habits over the lifespan. Children with congenital heart disease (CHD) may be limited in their ability to participate in sport due to delayed gross motor development, physiological, psychological, or medical limitations. We sought to understand how many of our CHD patients participate in sport. METHODS: A single-centre retrospective review (May 2016-October 2023) was conducted in CHD patients with a cardiopulmonary exercise test (CPET) and an assessment of sport participation. Criteria for a maximal CPET test included: a respiratory exchange ratio > 1.0, and/or a peak heart rate > 200 bpm. VO2peak and HR z-scores were calculated. Sport participation was recorded based on frequency, intensity, time, and type and was categorized into 3 groups: Participation (at least 2 days/week); No sport participation but regular activity; No participation. Frequency tables were generated. A one-way ANOVA with a Tukey post-hoc test was used to determine differences between groups. P < 0.05 was considered statistically significant. RESULTS: Seventy-four CHD patients were included: 8 with aortic valve disease (no intervention); 12 with coarctation; 6 following the Ross procedure for aortic valve stenosis; 25 with tetralogy of Fallot, 11 with transposition of the great arteries; and 12 with Fontan palliation. Forty-six percent of our study cohort were involved in sport, 19% regularly attended the gym or achieved >10,000 steps/day and 35% did not participate in any sport. There was no difference in HR z-score (-0.77 vs -1.03 vs -0.73; p ≥ 0.05) or peak HR (190 vs 186 vs 183 bpm; p ≥ 0.05) for those who participate, do not participate but are active, or those who do not participate in sport, respectively. VO2peak z-score was different between groups (p = 0.002) with the lowest z-score in those who do not participate in sport compared to those who do participate in sport (-1.63 vs -0.77; p < 0.001). CONCLUSIONS: Thirty-five percent of our patient group did not participate in sport and had a lower VO2peak z-score compared to those who did participate in sport. Understanding the barriers to sport participation in this population may provide important insight and direct intervention strategies.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.307
Teacher spread0.293 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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