Retrospective Study of Risk Factors and the Prevalence of Injuries in HIFT
Bibliographic record
Abstract
The aim of this study was to investigate the risk factors and the incidence of injuries in high-intensity functional training (HIFT) practitioners. A survey was administered to 213 HIFT practitioners. Participants reported the number of injuries, the location of the injuries, and training exposure during the preceding six months and answered questions regarding potential risk factors for injury. We found there were 7.1 injuries for every 1000 hours of training. In addition, we found that individuals with experience in the modality (>2 years) were 3.77 times more likely to be affected by injury when compared with beginner individuals (<6 months) (CI95%=1.59-8.92; p=0.003). When the analysis was performed only for the competitive level, we found that practitioners competing at the national level were 5.69 times more likely to experience an injury than competitors who do not compete (CI95%=1.10-29.54; p=0.038). We also found that the injuries mainly affect the shoulder and lumbar regions. It was possible to conclude that subjects with a higher level of experience in the modality are more likely to be affected by injuries and that the shoulder and lumbar areas are most likely to be injured during HIFT.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".