The influence of high-intensity interval running bouts on distal anterior femoral cartilage in competitive distance and middle-distance runners
Bibliographic record
Abstract
Competitive runners compared with recreational runners have increased odds of osteoarthritis and running-related injury, potentially from different running types. We compared distal anterior femoral cartilage deformation in competitive runners following a continuous and high-intensity interval run (10 × 400 m, 300 m jog) and evaluated the association between running kinetics and cartilage deformation. Twenty-four competitive runners (11 females and 13 males), between 18 and 35 years old underwent femoral cartilage ultrasound imaging before and after both running conditions in a counterbalanced order 2–7 days apart. Footwear was instrumented with force-sensing insoles to extract peak ground reaction force, loading rate, and impulse. A 2 (time) by 2 (condition) ANOVA with repeated measures evaluated the change in cartilage thickness after running between conditions. The lateral cartilage region showed greater deformation after interval compared with continuous running (p = 0.003). A main effect of time was seen where cartilage was thinner after running compared with baseline regardless of condition (1.92 (1.82, 2.02) vs. 1.83 (1.73, 1.93) mm; mean difference = −0.094 (−0.147, −0.042) mm, p = 0.001). No significant associations were found between cartilage deformation and loading rate, peak ground reaction force, or impulse (all r < 0.32, all p > 0.05). Interval running contributed to greater lateral distal anterior femoral cartilage deformation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".