Assessment of selected anthropometric indicators and training parameters in runners and their impact on injury risk and musculoskeletal overloading
Bibliographic record
Abstract
Background: Running, as a key contributor to overall health, enhances performance, fortifies muscles, and aids in maintaining a healthy body weight.Nevertheless, it is crucial to acknowledge that, akin to any physical activity, running entails a particular risk of injury, particularly affecting the lower extremities.Aims: The study aimed to identify factors predisposing to musculoskeletal injuries and their most common locations among amateur runners.Material and methods: A comprehensive study was conducted involving 116 amateur long-distance runners aged 18 to 69 years.The research utilized three distinct questionnaires to gather valuable insights into the runners' musculoskeletal health and training experiences.The Nordic Musculoskeletal Questionnaire (NMQ) provided a systematic assessment of musculoskeletal discomfort and symptoms, while the Oslo Sports Trauma Research Center Questionnaire (OSTRCQ) focused on gathering data related to sports-related injuries.A self-report questionnaire was also employed to delve into individual perspectives and subjective experiences.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".