Suspected Mechanisms in the Cause of Overuse Running Injuries: A Clinical Review
Bibliographic record
Abstract
CONTEXT: Various epidemiological studies have estimated that up to 70% of runners sustain an overuse running injury each year. Although few overuse running injuries have an established cause, more than 80% of running-related injuries occur at or below the knee, which suggests that some common mechanisms may be at work. The question then becomes, are there common mechanisms related to overuse running injuries? EVIDENCE ACQUISITION: RESEARCH STUDIES WERE IDENTIFIED VIA THE FOLLOWING ELECTRONIC DATABASES: MEDLINE, EMBASE PsycInfo, and CINAHL (1980-July 2008). Inclusion was based on evaluation of risk factors for overuse running injuries. RESULTS: A majority of the risk factors that have been researched over the past few years can be generally categorized into 2 groups: atypical foot pronation mechanics and inadequate hip muscle stabilization. CONCLUSION: Based on the review of literature, there is no definitive link between atypical foot mechanics and running injury mechanisms. The lack of normative data and a definition of typical foot structure has hampered progress. In contrast, a large and growing body of literature suggests that weakness of hip-stabilizing muscles leads to atypical lower extremity mechanics and increased forces within the lower extremity while running.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".