Head Injuries in Rock Climbing: A Scoping Review
Bibliographic record
Abstract
Rock climbing was recognized as a sport at the 2020 Tokyo Olympics. Despite its increasing participation, there is no knowledge synthesis of head injuries (HIs), defined as any injury to the head, associated with climbing, making it challenging for clinicians to provide evidence-based care. Our aim was to synthesize HI literature within rock climbing and identify knowledge gaps. Six databases (Medline, Embase, Sports Medicine & Education Index, SPORTDiscus, CINAHL, and Cochrane) were searched. Two reviewers screened 345 studies and 31 studies were selected for data abstraction. We found the quality of individual studies mainly "fair" to "good." Both HI and traumatic brain injury (TBI) had inconsistent definitions and categorization. The HIs represented between 0 to 36% of reported climbing injuries. Between 11 to 100% of HIs were TBIs, defined as an HI with permanent or temporary neurological sequelae. The most common causes of HIs were outdoor falls and falling objects. Climbing-specific factors associated with the causes were infrequently examined in the literature. Data sources of safety practices were incomplete. Overall, there was a lack of literature examining HIs, mechanisms of injury, and safety practices associated with climbing. To improve the tracking of HIs in climbing, we suggest the use of consistent reporting standards and the creation of a climbing injury surveillance system.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".