Bibliographic record
Abstract
OBJECTIVES: To identify all studies of gymnastics injuries and assess injury rates, types, locations, and causes. METHODS: Seven electronic and two grey literature databases were searched. Two reviewers independently assessed titles/abstracts, abstracted data, and calculated average rates weighted by study size. RESULTS: One study (n = 963) of three Olympic games (2008,2012,2016) provided injury rates for females of 86.4/1000gymnasts and males 79.9. For 29 databases one study of males (n = 64) provided a rate of 8.8 injuries/1000hours/AE (AE = athletic-exposure) and three of females varied rates (8.5, 9.4, and 91). Three studies for males (n = 153) provided a weighted average rate of 1.4 injuries/1000hours/training, and for females six studies (n = 476) 1.5 injuries. Four studies of males (n = 286) provided a weighted average of 678 injuries/1000gymnasts per year and eight studies of females (n = 1,764) a rate of 306. Of 19 surveys, eight provided rates for females (n = 1,463) of 596 injuries/gymnast per year and two for males (n = 40) a rate of 1,036. In the Olympics injuries were preponderately lower limb (63%) then trunk (23%) and upper limb (14%); injury type was sprains (35%) followed by tendinopathy/arthritis/impingements (17%), contusions (10%), and fractures (7%). Five database studies provided injury location data for 274 males: averages weighted by study size were upper extremity 42.8%, lower extremity 33.6%, torso/spine 11.8%, and head/neck 4.9% and 12 studies with 843 females provided average rates for lower extremity 51%, upper extremity 30.8%, torso/spine 13% and head/neck 0.8%. Official gymnastics organizations' websites provide no readily available data about injury rates or methods of prevention. CONCLUSIONS: Studies need to collect comprehensive data for injury rates by training/competitions, gender, age, injury location/type/cause. Studies could assess whether trainers and physiotherapists monitoring gymnasts closely for injury risk would reduce injuries. Studies including randomized controlled trials (RCTs) of interventions in training, videotaping and performance feedback to reduce injury rates would be helpful.
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 | 0.000 |
| 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".