Stroke and Athletes: A Scoping Review
Bibliographic record
Abstract
Stroke (i.e., cerebrovascular accident) affects one in 10,000 people between the ages of 14 and 45; however, very little is known about the frequency and type of stroke that occurs in athletes. The risk of injury to the neurovascular structures may depend on the type of sport involvement, although, sport-specific incidence rates are not known. Therefore, the goal of our scoping review was to provide some guidance to better inform the development of a context-fit stroke model by summarizing studies on a broad research topic related to stroke or cerebrovascular accident in sport based on a strict athlete definition. We used the guidance of Arksey and O'Malley's five-stage-process for a scoping review. Databases included MEDLINE(R) Epub Ahead of Print, In-Process & Other Non- Indexed Citations, Ovid MEDLINE(R) Daily and Ovid MEDLINE(R), and Embase (OVID databases); CINAHL Plus with Full Text, SportDiscus with Full Text (Ebsco databases); and Scopus. Publication dates were from 1979-2020 across nine different countries resulting in 39 individual cases of stroke with an athlete age range of 14-56 years (95% male). The major inciting event(s) prior to stroke onset were headaches (38.4%), head trauma (30.7%), and neck injury and/or vertebral artery dissection (20.5%). Several sporting activities were represented with American football as the most prevalent (30.7%). In summary, we found that sports with an aspect of impact, collision, or microtrauma can lead to subsequent stroke. These sport-related traumatic events were often difficult to diagnose because of the longer interval before ischemia occurred. Therefore, health care providers should be particularly attuned to the possibility of stroke when evaluating athletes presenting with or without neurological deficit.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".