Investigating the impact of dehydration and hydration on In-Vivo hip soft tissue biomechanics
Bibliographic record
Abstract
Hip soft tissue biomechanics has a significant effect on hip loading and fracture risk during falls. Despite the high dehydration rate in older adults, athletes, and outdoor workers and its association with a higher risk of falls, the effect of dehydration on hip soft tissue biomechanics is unknown. Twenty participants (13 females and 7 males, aged 18-35) underwent indentation tests and ultrasound imaging over the greater trochanter in both hydrated and dehydrated states. We assessed the hydration levels using a urine color chart and measured the tissue thicknesses via ultrasound. Results showed a significant increase in peak force (from 12.67 ± 9.09 N to 15.46 ± 9.23 N, p < 0.05) under dehydration. We observed notable sex differences, with males exhibiting higher stiffness and energy absorption than females, despite variations in peak force. Fat thickness emerged as a critical predictor of biomechanical response, particularly in the dehydrated state. These findings underscore the importance of hydration in maintaining soft tissue integrity and reducing injury risks. Future work should explore chronic dehydration effects and include broader demographic variations to enhance fall prevention strategies and clinical practice. This research also highlights the need for targeted hydration management in at-risk populations.
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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".