The Effect of Age on Fat Distribution in the Neck Using Volumetric Computed Tomography
Bibliographic record
Abstract
BACKGROUND: Neck fat distribution plays an important role in aging, yet how fat distribution changes with age is largely unknown. This study used volumetric computed tomography in live patients to characterize neck fat volume and distribution in young and elderly women. METHODS: A retrospective analysis was conducted of head and neck computed tomographic angiographs of 20 young (aged 20 to 35 years) and 20 old (aged 65 to 89 years) women. Fat volume in the supraplatysmal and subplatysmal planes was quantified. Distribution of fat volume was assessed by dividing each supraplatysmal and subplatysmal compartment into upper, middle, and lower thirds. RESULTS: Total supraplatysmal fat volume was greater than subplatysmal in all patients. Young patients had more total supraplatysmal fat than old patients (p < 0.0001). No difference was found between age groups in subplatysmal fat (p > 0.05). No difference was found between upper/middle/lower third supraplatysmal fat volumes in young patients. When comparing supraplatysmal thirds within the elderly population, the middle third fat volume (28.58 ± 20.01 cm3) was greater than both upper (18.93 ± 10.35 cm3) and lower thirds (15.46 ± 11.57 cm3) (p < 0.01). CONCLUSIONS: This study suggests that total supraplatysmal fat volume decreases with age. Older patients had more fat volume in the upper and middle thirds compared with the lower third of the supraplatysmal fat compartment, whereas young patients had more evenly distributed fat. These results suggest that fat deposition and redistribution in the neck occur with age and may be a contributing factor to the obtuse cervicomandibular angle of the elderly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".