Differences in Lean Body Mass and Muscle Thickness of Young and Older Males
Bibliographic record
Abstract
1398 It is well known that the size of individual muscles decline with age. However, it is difficult to determine which muscles are affected the most by age because few studies have compared multiple muscle groups between young and older individuals. PURPOSE: To determine lean body mass and muscle thickness of the elbow, knee, and ankle flexors and extensors in young and older males. METHODS: Young (n = 20, 23 yr, 82.1 kg) and older (n = 26, 65 yr, 82.8 kg) healthy males with similar body mass who were not resistance training (> 2 months) were evaluated. Lean body mass was determined using air displacement plethysmography and muscle thickness of the biceps brachii, triceps brachii, vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius was measured using ultrasound. RESULTS: Compared to older males, young males had more lean body mass (64.7 kg vs. 58.5 kg, p<0.01), and greater muscle thickness (p<0.01) for the biceps brachii (3.3 cm vs. 2.7 cm), vastus lateralis (4.3 cm vs. 3.8 cm), biceps femoris (5.6 cm vs. 4.6 cm), tibialis anterior (2.7 cm vs. 2.1 cm), and gastrocnemius (4.5 cm vs. 3.3 cm). The only muscle group that did not differ in thickness between age groups was the triceps brachii (young = 4.0 cm, old = 3.8 cm). CONCLUSION: Lean body mass and muscle thickness is negatively affected by age. The magnitude of the age effect may depend on the muscle group being assessed.
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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 | 0.000 |
| 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) | 0.002 | 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".