Abstract 10977: Shared Decision-Making in Athletes Diagnosed With a Cardiovascular Condition: A Scoping Review
Bibliographic record
Abstract
Introduction: Exercise restriction following the identification of a cardiovascular condition can profoundly impact the identity, career, and well-being of athletes. Shared decision-making (SDM) is emerging as the standard of care to guide recommendations for athletes at risk of cardiovascular events. This scoping review summarizes existing approaches, barriers, and facilitators to SDM in sports cardiology. Methods: A literature search of the MEDLINE, Embase, Cochrane Library, PubMed, CINAHL, SPORTDiscus, and PsycInfo databases was conducted in January 2022. Abstract screening and full-text review were completed in duplicate by independent reviewers. The PCC (Participants, Concepts, Context) framework was used to assess study eligibility. Included articles discussed the use of SDM (C/C) following the diagnosis of a cardiovascular condition in an athlete (P). Results: A total of 6,049 records were screened, of which 38 were included in this review. Article classifications included theoretical papers (31), such as editorials and guidelines, and research studies (7). Main findings of the research studies are shown in Table 1. All selected articles defined SDM as an open dialogue between the athlete, healthcare team, and other stakeholders (e.g., parents, coaches). The benefits and risks of management strategies, treatment options, and return-to-play were the focus of this dialogue. Common themes among SDM approaches emerged, such as emphasizing patient values, considering non-physical factors (e.g., emotional, psychological, financial), and informed consent. Barriers to SDM included pressure from institutions and liability of healthcare providers in the case of adverse events. Conclusions: SDM is the recommended paradigm for providing care to athletes diagnosed with cardiovascular conditions. Further education for healthcare providers regarding the SDM approach, as well as future research assessing SDM in a clinical setting, is needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".