Risk factors for injury in sport climbing and bouldering: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Rock climbing is an increasingly popular sport worldwide, as a recreational activity and a competitive sport. Several disciplines including sport climbing and bouldering have developed, each employing specific movements and techniques, leading to specific injuries. OBJECTIVE: To examine risk factors and prevention measures for injury in sport climbing and bouldering, and to assess the methodological quality of existing studies. METHODS: 12 electronic databases and several other sources were searched systematically using predetermined inclusion and exclusion criteria. Eligible articles were peer-reviewed, based on primary research using original data; outcome measures included injury, morbidity or mortality in rock climbing, and included one or more potential risk factor or injury prevention strategy. Two independent reviewers assessed the methodology of research in each study using the Downs and Black Quality Index. The data extracted is summarised, and appraisals of the articles are presented with respect to the quality of evidence presented. RESULTS: 19 studies met the inclusion criteria, and introduced 35 possible risk factors or injury prevention measures in climbing. Age, increasing years of climbing experience, highest climbing grade achieved (skill level), high climbing intensity score (CIS) and participating in lead climbing are potential risk factors. Results regarding injury prevention measures remain inconclusive. DISCUSSION: This field is relatively new and, as such, the data are not as robust as for more established sports with a larger research foundation. The key need is establishing modifiable risk factors using prospective studies and high quality methodology, such that injury prevention strategies can be developed. The CIS may be a useful measure in this field of research.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| 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 | 0.001 |
| 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".