Prevalence, Causes and Comparison of Lower Extremities Injuries of Elite Male Athletes in Handball, Football and Basketball
Bibliographic record
Abstract
Epidemiology research, particularly in sport is one of the essential tools to identify injuries and strategies for the prevention of sports injuries can be designed and formulated by specifying them. The present study is practical in terms of objective and it is descriptive-field and retrospective in terms of data collection. The statistical population of this research consists of all male athletes in handball, basketball and football in Khuzestan province. The statistical sample consists of 36 handball players, 40 football players and 32 basketball players. Data analysis was done using one-way analysis of variance and multiple regression using SPSS version 18. The result of one-way analysis of variance showed that a significant difference exists among the total injuries to lower extremities (F = 172.2; p= 0.00), knee (F = 10.6; P = 0.00), ankle (F = 9.4; P = 0.00) in the three groups. Tukey post hoc test results revealed that in all three cases, handball injuries were most prevalent, followed by basketball, and football.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".