Differences in Temporal Volume between Males and Females and the Influence of Age and BMI: A Cross-Sectional CT-Imaging Study
Bibliographic record
Abstract
Abstract Background The temple has been identified as one of the most compelling facial regions in which to seek aesthetic improvement—both locally and in the entire face—when injecting soft tissue fillers. Objective The objective of this study is to identify influences of age, gender, and body mass index (BMI) on temporal parameters to better understand clinical observations and to identify optimal treatment strategies for treating temporal hollowing. Methods The sample consisted of 28 male and 30 female individuals with a median age of 53 (34) years and a median BMI of 27.00 (6.94) kg/m2. The surface area of temporal skin, the surface area of temporal bones, and the temporal soft tissue volume were measured utilizing postprocessed computed tomography (CT) images via the Hausdorff minimal distance algorithm. Differences between the investigated participants related to age, BMI, and gender were calculated. Results Median skin surface area was greater in males compared with females 5,100.5 (708) mm2 versus 4,208.5 (893) mm2 (p < 0.001) as was the median bone surface area 5,329 (690) mm2 versus 4,477 (888) mm2 (p < 0.001). Males had on average 11.04 mL greater temporal soft tissue volume compared with age and BMI-matched females with p < 0.001. Comparing the volume between premenopausal versus postmenopausal females, the median temporal soft tissue volume was 46.63 mL (11.94) versus 40.32 mL (5.69) (p = 0.014). Conclusion The results of this cross-sectional CT imaging study confirmed previous clinical and anatomical observations and added numerical evidence to those observations for a better clinical integration of the data.
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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 | 0.001 |
| 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) | 0.003 | 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".