Classification of higher- and lower-mileage runners based on running kinematics
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Running-related overuse injuries can result from the combination of extrinsic (e.g., running mileage) and intrinsic risk factors (e.g., biomechanics and gender), but the relationship between these factors is not fully understood. Therefore, the first purpose of this study was to determine whether we could classify higher- and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. The second purpose was to subgroup the runners by gender and determine whether we could classify higher- and lower-mileage runners in male and female subgroups. METHODS: = 40 (29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed (2.61 ± 0.23 m/s). A support vector machine classifier identified kinematic differences between higher- and lower-mileage groups based on principal component scores. RESULTS: Higher- and lower-mileage runners (both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender, higher- and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher- and lower-mileage male runners could be separated with 100% classification accuracy. CONCLUSION: These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it