Epidemiology of Lateral Ligament Complex Tears of the Ankle in National Collegiate Athletic Association (NCAA) Sports: 2014-15 Through 2018-19
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies of lateral ankle sprains in NCAA sports are important in appraising the burden of this injury and informing prevention efforts. PURPOSE: To describe the epidemiology of lateral ankle sprains in NCAA sports during the 2014-15 through 2018-19 seasons. STUDY DESIGN: Descriptive epidemiology study. METHODS: Injury and exposure information collected within the NCAA Injury Surveillance Program (ISP) were examined. Counts, rates, and proportions of lateral ankle sprains were used to describe injury incidence by sport, event type (practices, competitions), season segment (preseason, regular season, postseason), injury mechanism (player contact, noncontact, and surface contact, injury history (new, recurrent), and time loss (time loss [≥1 day], non-time loss). Injury rate ratios (IRRs) were used to examine differential injury rates, and injury proportion ratios (IPRs) were used to examine differential distributions. RESULTS: A total of 3910 lateral ankle sprains were reported (4.61 per 10,000 athlete exposures) during the study period, and the overall rate was highest in men's basketball (11.82 per 10,000 athlete exposures). The competition-related injury rate was higher than the practice-related rate (IRR, 3.24; 95% CI, 3.04-3.45), and across season segments, the overall rate was highest in preseason (4.99 per 10,000 athlete exposures). Lateral ankle sprains were most often attributed to player-contact mechanisms in men's (43.2%) and women's sports (35.1%), although injuries were more prevalently attributed to player contact in men's than in women's sports (IPR, 1.23; 95% CI, 1.13-1.34). Overall, 49.7% of all lateral ankle sprains were time loss injuries. CONCLUSIONS: The findings of this study are consistent with previous epidemiological investigations of lateral ankle sprains among NCAA athletes. Results offer additional context on differential injury mechanisms between men's and women's sports and on injury risk across the competitive season. Future research may examine the effectiveness of deploying injury prevention programs before the start of a season.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".