Injuries in male and female semi-professional football (soccer) players in Nigeria: prospective study of a National Tournament
Bibliographic record
Abstract
BACKGROUND: Research on the epidemiology of football injuries in Africa is very sparse despite its importance for injury prevention planning in a continent with limited sports medicine resources. The vast majority of studies available in literature were conducted in Europe and only a very few studies have prospectively reported the pattern of football injury in Africa. The purpose of this study was to evaluate the incidence and pattern of injuries in a cohort of male and female semi-professional football players in Nigeria. METHODS: A prospective cohort design was conducted, in which a total of 756 players with an age range of 18-32 years (356 males and 300 females) from 22 different teams (12 male and 10 female teams), were prospectively followed in a National Football Tournament. Physiotherapists recorded team exposure and injuries. Injuries were documented using the consensus protocol for data collection in studies relating to football injury surveillance. RESULTS: An overall incidence of 113.4 injuries/1000 h (95% CI 93.7-136.0) equivalent to 3.7 injuries/match and time-loss incidence of 15.6 injuries/1000 h were recorded for male players and 65.9 injuries/1000 h (95% CI 48.9-86.8) equivalent to 2.2 injuries/match and time-loss incidence of 7.9 injuries/1000 h were recorded for female players. Male players had a significantly higher risk of injuries [IRR = 1.72 (95% CI 1.23-2.45)]. Injuries mostly affected the lower extremity for both genders (n = 81, 70% and n = 31, 62% for males and females respectively). Lower leg contusion (n = 22, 19%) and knee sprain (n = 9, 18%) were the most common specific injury types for male and female players respectively. Most of the injuries were as a result of contact with another player (n = 102, 88%-males; n = 48, 96%-females). Time-loss injuries were mostly estimated as minimal (n = 11, 69%) for male players and severe (n = 4, 66%) for female players. CONCLUSION: The overall incidence of injuries among Nigerian semi-professional football players is high but most of the injuries do not result in time-loss. Pattern of injuries is mostly consistent with previous studies. More prospective studies are needed to establish injury prevention initiatives among African players.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".