Bibliographic record
Abstract
OBJECTIVES: to identify all studies of Karate injuries and assess injury rates, types, location, and causes. METHODS: Six electronic and four grey literature databases were searched. Two reviewers independently assessed titles/abstracts, abstracted data and assessed risk-of-bias with the Newcastle-Ottawa scale. Average injury rates/1000AE (AE = athletic-encounter) and/1000minutesAE, injury location and type weighted by study size were calculated. RESULTS: In competitions rates of injury/1000AE and/1000 minutesAE were similar for males (111.4/1000AE, 75.4/1000 minAE) and females (105.8/1000AE, 72.8/1000 minAE). Location of injury rates/1000AE for males were 44.0 for head/neck, 11.9 lower extremities, 8.1 torso and 5.4 upper extremities and were similar for females: 41.2 head/neck, 12.4 lower extremities, 9.1 torso and 6.3 upper extremities. Injury rates varied widely by study. Rates/1000AE for type of injury were contusions/abrasions/lacerations/bruises/tooth avulsion for males (68.1) and females (30.4); hematomas/bleeding/epistaxis males (11.4) and females (12.1); strains/sprains males (3.5) and females (0.1); dislocations males (2.9) and females (0.9); concussions males (2.5) and females (3.9); and fractures males (2.9) and females (1.4). Punches were a more common mechanism of injury for males (59.8) than females (40.8) and kicks similar (males 19.7, females 21.7). Weighted averages were not calculated for weight class or belt colour because there were too few studies. Nineteen injury surveys reported annual injury rates from 30% to rates ten times higher but used different reporting methods. Studies provided no data to explain wide rate ranges. CONCLUSIONS: Studies need to adopt one injury definition, one data-collection form, and collect comprehensive data for each study for both training and competitions. More data are needed to measure the effect of weight, age and experience on injuries, rates and types of injury during training, and for competitors with high injury rates. RCTs are needed of interventions such as training and feedback of performance data to reduce injury rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".