Impact of deep vein thrombosis on adolescent athletes: Navigating an invisible disability
Bibliographic record
Abstract
Background: Injury in adolescent athletes that threatens their sport participation can result in a sense of identity loss during critical years for identity development, creating the potential for significant mental health challenges. The specific effect of deep vein thrombosis (DVT) in this vulnerable population has not been characterized. Purpose: To describe the impact of DVT diagnosis, treatment, and long-term complications on the mental well-being of athletes who sustained a DVT during adolescence and to identify strategies to improve the quality of care for these patients. Methods: Using a qualitative study design, athletes with a history of DVT during adolescence and their parents were recruited to participate in semistructured interviews. Interviews were transcribed and analyzed using thematic analysis. Participants were recruited until reaching thematic saturation. Results: In total, 19 participants (12 athletes, 7 parents) were recruited. Athletes were mainly males (67%), median age at time of DVT was 15 years (range, 12-18 years), and median age at study participation was 19 years (range, 16-34 years). Thematic analysis revealed four main themes: Theme 1: DVT posed a threat to sport participation; Theme 2: at a personal level, there were significant mental health challenges; Theme 3: at a societal level, DVT is an invisible disability; and Theme 4: physical, psychological, and transition support are important to improve the care of these patients. Conclusion: Deep vein thrombosis threatens an athlete's participation in sport, resulting in a significant and complex impact on their mental well-being. Heightened awareness and a multidisciplinary approach are needed to help young athletes navigate the consequences of DVT.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".