Editorial: Injuries, injury prevention and training in climbing - volume II
Bibliographic record
Abstract
EditorialInjuries, injury prevention and training in climbingVolume 2Climbing as a sport has matured. Today, we see a rise in evidence based on systematic training, elevated level of performance, and science is closing scientific gaps regarding injury prevention and treatment. Appearance in two Olympics has shaped the future athlete’s perspective of what is achievable in climbing. Still, for the vast majority, climbing is a recreational unorganized activity making it impossible to quantify the total number of people attending climbing on a regular basis. For the research on climbing the maturity is shown in the introduction of the research papers; it is no longer a need to explain what climbing is, how it is organised and how it has grown in the past decade. The different disciplines of climbing demands different morphology, different training regimes, and risk factors of different potential injuries. As climbing as a sport becomes more specialised, the research must follow along. It is about time to present the research on climbing with clear distinction on what discipline the respondents or subjects are attending and training for. Furthermore, the results of the research needs to be presented with analyses based on gender and levels of performance. This way of presenting results and discussion will make the research easier to apply to the relevant groups. Randomized controlled trials is rarely included study design in the climbing literature, until now. It is therefore great to see Saeterbakken et al presenting one of few randomized controlled trials examining climbing training in this research topic. We hope to see more of these in the future.The quality of the research on climbing have been improving during the last decade with new areas, new perspective, and with new methodology. The research is also increasingly globalised. In this research topic we are presenting eight papers from a total of 32 researchers representing most of the continents. This research topic covers a thematic span from how to optimize training, what to focus on while training, how to treat injuries, and potential barriers to seeking treatment as well as an insight on optimal body composition for bouldering.A topic that has been following in the footsteps of climbing since early morning is what it takes to become a top-level climber. A never-ending debate in the world of climbing is the importance of height. Is it favourable to be tall or not? These questions and more are answered in a study of the morphology of the World Cup elite boulderers and comparing them with national level climbers and a group of students (Draga et al). Some might find the results surprising while others will have their view confirmed.Another topic that seems to make headlines is the relative high level of eating disorders and potential Low Energy Availability (LEA) or Relative Energy Deficit (RED-S) among climbers. An interesting study is examining the possible interactions between social media use and food tracking behaviours (Slagel, Kage and Wichern). The authors recommended campaigns in social media to help climbers keep a healthy body image to avoid potential health issues and injuries. Importantly, the study included recreational climbers meaning these findings is not only related to high performance athletes but everyday men/women you meet in the climbing gym or at the crag. By presenting new research in a research topic, we can see several papers together. In this topic we can see the broader picture when combining the new research on what is the most important feature for predicting performance (Draga), how to perform the training (Saeterbakken et al), and how to optimize recovery (Kruoková, Tufano, Balás). All these three papers are advances in their own respect, but gives a deeper insight read together as a total. Injuries in climbing are sadly rather common and well documented in previous research. These injuries are well documented in previous research. However, the novelty of the growing body of evidence is how to treat the injuries. In this topis we can present treatment algorithms for two common finger injuries in climbing. Schöffl et al examined a One treatment algorithm for capsulitis (Schöffl et al) and synovial chondromatosis (Becker et al ).Sadly, climbers do not seek or use professional health care as often as they should. The lack of trust in health care seems to be a phenomenon that repeats itself across the globe. In this research topic one of the studies are examining the barriers for health seeking behaviour in Manilla (Cruz, Cabrera). This paper is also one of few that are studying only those who focus on indoor climbing.To summon up, we are proud to present the second edition of the research topic Injuries, injury prevention and training in climbing. This research topic aimed to gain new insight in topics relevant for injury, injury prevention and training methodology in the different disciplines of climbing. On behalf of the editorial team and with the help from the researchers who chose to publish their research in this topic I am proud to say that we achieved our goal.We are aiming to open up for a third volume of this topic in early 2026. In the next volume we hope to see new research to gain more insight on climbing injuries, injury prevention and training. The research has the potential to help the sport of climbing to reach new heights and fulfil its potential, without injuries!Gudmund Grønhaug Pt, Msc, PhDGuest editor
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.029 | 0.017 |
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".