Hip Fractures: Clinical, Biomaterial and Biomechanical Insights into a Common Health Challenge
Bibliographic record
Abstract
Hip fractures represent a significant public health challenge, particularly among the elderly, due to their high incidence, morbidity, and mortality rates. This review provides a comprehensive understanding of hip fractures through clinical, biomaterial, and biomechanical perspectives. Clinically, we examined key risk factors, including age, bone mineral density, and the high prevalence of falls, which account for over 95% of hip fractures. However, current clinical tools, such as FRAX, have notable limitations in accurately assessing fracture risk in individuals due to their reliance on statistical models, the treatment of interdependent risk factors as independent, and the omission of key variables like diabetes. From a biomaterial perspective, we analyzed bone composition-specifically the balance of inorganic minerals, organic proteins, and water-and its role in determining bone strength and fracture susceptibility. Various risk factors ultimately influence this composition balance, thereby affecting bone strength. Therefore, accurately measuring bone composition may provide a more reliable assessment of hip fracture risk. Although emerging imaging technologies such as dual-energy CT and MRI show promise for in vivo assessments of bone composition, these techniques still face significant challenges and remain an active area of research. Biomechanically, we explored the forces generated during falls, noting that impact forces can vastly exceed normal physiological loads and may exploit the anisotropic properties of bone, leading to fractures even in healthy individuals with strong bones. This understanding emphasizes the critical role of fall prevention in reducing fracture risk and highlights the limitations of using fall-induced fracture incidence as a validation metric for clinical assessment tools. Lastly, we discuss preventive strategies, including passive measures like environmental modifications for individuals diagnosed with low bone strength and proactive measures such as muscle strengthening and cognitive training. While passive measures are necessary for immediate protection, proactive strategies are more effective in the long term by addressing underlying risk factors for falls and promoting sustained bone health. This interdisciplinary review underscores the need to integrate clinical, biomaterial, and biomechanical factors to improve diagnostic accuracy, prevention, and treatment strategies for hip fractures, ultimately advancing public health outcomes in aging populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".