Biomechanics associated with bone stress injury in athletes differ by proximal and distal anatomical locations: a cross-sectional analysis
Bibliographic record
Abstract
Background: Bone stress injury (BSI) is a common overuse injury in female athletes that can occur in a variety of bones, including both proximal (pelvis, sacrum, femoral neck) or distal (tibia, fibula, metatarsals) locations. Prior work has demonstrated differences in running biomechanics in those with BSI; however, this was not separated by anatomy. We hypothesised that both female athletes with distal BSI and female athletes with proximal BSI would have lower cadence, higher centre of mass (COM) and lower duty factor than those without prior BSI. Methods: Cross-sectional study of 45 female athletes (15 with prior distal BSI, 15 with prior proximal BSI and 15 with no BSI history). Each ran on an instrumented treadmill at self-selected and 5-kilometre race speeds, with data collected in a fresh and exerted state. A series of analysis of variance tests (ANOVAs, group by condition) were used to analyse the results. Results: Participants with previous proximal BSI ran with greater vertical COM excursion compared with those with no previous BSI at race speed (10.2±1.7 cm vs 8.5±0.8 cm (p<0.001)). The proximal BSI population had a lower cadence than the no prior BSI population at race speed (170±13 steps per minute vs 180±10 steps per minute (p=0.012)). Duty factor was lower in the proximal BSI group compared with the distal BSI group at the race speed (32±3% vs 34±3% (p=0.013)). Conclusion: COM and cadence should be further investigated for association with proximal BSI.
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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 | 0.001 |
| 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) | 0.002 | 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".