Risk factors and diagnostic approaches to lower extremity stress fractures in athletes: Systematic review
Bibliographic record
Abstract
ABSTRACT Background: Stress fractures (SF) are frequent overuse injuries, with the lower extremities accounting for up to 95% of cases. Lower extremities SFs (LESFs) are common injuries among athletes who play endurance sports with high loads. Objective: The purpose of this review is to determine the risk factors, diagnosis, and management of athletes’ LESF. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta Analyses criteria, a thorough computerized literature search was carried out using keywords associated with the diagnosis, treatment, and risk factors of LESF in four electronic databases: Cochrane, Web of Science, Embase, and PubMed. Newcastle–Ottawa scale and JBI were utilized for the methodological quality assessment, whereas STATA software was employed for statistical analysis. Pooled effects on clinical outcomes were determined by calculating event rate and mean with the corresponding 95% confidence intervals (CI) using a random effects model. Results: Twenty studies were included in the final review. Risk factors including use of hormonal contraceptives 0.21 (95% CI; −0.52–0.95), menstruation dysfunction 0.25 (95% CI; 0.06–0.44), dietary behaviors Eating Disorder Examination Questionnaire scores of 1.01 (95% CI; 0.68–1.34), training duration and regimen, sex, bone mineral density, and prior SF were associated with increased risk of suffering LESF. While magnetic resonance imaging is the gold standard for diagnosing suspected LESF, other diagnoses include X ray, computed tomography scan, and scintigraphy. Conclusion: The causes of LESF in athletes are complex and extensive. Multicenter epidemiological prospective studies are required to accurately identify the ideal preventative and therapeutic measures and reconcile conflicting data about diet and training.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".