The Effect of Age on Tibia and Fibula Cross-sectional Areas in Young, Old, and Very Old Men
Bibliographic record
Abstract
It is well established that changes in bone geometry occur with age. However, there has been little investigation about the influence of weight-bearing load on the magnitude of these changes. PURPOSE: To assess the impact of aging on cross-sectional areas of the main weight-bearing (tibia) and minimally weight-bearing (fibula) bones of the leg. METHODS: Serial transverse magnetic resonance images of the right leg were acquired in 13 young (26 ± 3y), 13 old (66 ± 2y), and 13 very old men (83 ± 4y) of similar heights and weights. Cortical area (CA) and medullary area (MA) of the tibia and fibula were measured for 5 images (7mm slice thickness, 3mm slice offset) centered one-third of the distance between the head of the fibula and the lateral malleolus. Total area (TA) for each image was calculated as the sum of CA and MA, and the relative percentage of cortical bone (CORT) was calculated as CA divided by TA. The mean of the 5 images was used for all statistical comparisons; significance set at P<0.05. RESULTS: In the tibia, CA was greater in young (434mm2) than old (377mm2) and very old (340mm2), whereas MA was greater in very old (265mm2) than young (178mm2); old (211mm2). Total bone was not different among the age groups (612, 588, and 604mm2 for young, old, and very old, respectively). Young and old had higher percentages (70% and 65%, respectively) of CORT than did the very old (57%), with no difference between young and old. In the fibula, CA was greater in young (109mm2) than very old (93mm2); old (95mm2). Medullary area was greater in very old (40mm2) than young (25mm2) and old (27mm2). Similar to the tibia, total bone of the fibula was unchanged with age (134, 122, and 133mm2 for young, old, and very old, respectively), and CA expressed as a percentage of the TAwas higher in young and old (81 % and 78%, respectively) as compared to very old (70%). CONCLUSION: Age-related decreases in CA and increases in MA led to a progressive decline in the relative percentage of CORT in both the tibia and fibula. These findings indicate that the weight-bearing role of the tibia does not prevent the age-related loss of CORT. In fact, the relative loss of CORT was slightly greater in the tibia than fibula which suggests that the weight-bearing role of the tibia makes it more susceptible than the fibula to the reduced activity typically associated with aging. Supported by NSERC
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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 | 0.001 | 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".