Differences In Psychological Distress And Health-related Quality Of Life Outcomes Among Collegiate Student-athletes With And Without A History Of Undiagnosed Concussion
Bibliographic record
Abstract
Due to lack of clinical care, undiagnosed concussions may impact clinical outcomes; however, little is known about the effects of undiagnosed concussions on patient reported outcomes. PURPOSE: To examine differences in psychological distress and health-related quality of life between collegiate athletes with and without a history of undiagnosed concussions. METHODS: A cross-sectional design (collected 2017-2019) included student-athletes at collegiate institutions in the United States and Canada (n = 6 institutions) who completed pre-season baseline testing as part of a larger clinical trial. Assessments relevant to the current study included demographics, concussion history, psychological distress (BSI-18), and health-related quality of life (PROMIS-29) questionnaires. The independent variable was previous history of at least one undiagnosed concussion (yes vs. no). Key outcomes included: BSI-18 total scores and PROMIS-29 scores for physical function, social roles, fatigue, anxiety, depression, sleep, pain intensity, and pain interference. Wilcoxon Rank Sum Tests were utilized to examine differences in BSI-18 total score and PROMIS-29 domain scores between those with and without a history of at least one undiagnosed concussion. Alpha level was set to 0.05 a priori. RESULTS: 1658 individuals completed pre-season baseline assessments. 597 (36.2%) reported a previous history of concussion and were included in the current study. Of those 597 (age = 19.7 ± 1.6 years), 77 (12.9%) reported at least one undiagnosed concussion, and 231 (38.7%) were female. On average there were 1.2 ± 0.6 undiagnosed concussions in this group. BSI-18 total scores were higher/worse (p = 0.023) in the undiagnosed [median (IQR) = 1(5)] compared to the diagnosed [median (IQR) = 0 (3)] group. PROMIS-29 fatigue scores were also significantly higher/worse (p = 0.023) in the undiagnosed [median (IQR) = 7.5 (7)] compared to the diagnosed [median = 6 (4)] group. CONCLUSIONS: Data suggests slightly worse psychological distress and fatigue in individuals with a history of at least one undiagnosed concussion. Future research is needed to understand how clinically impactful this relationship is and how dose-response of undiagnosed concussions affects outcomes over time. Supported by a grant from the National Football League
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".