Personal (Self) Perceptions of Submental Fat Among Adults in the United States
Bibliographic record
Abstract
BACKGROUND: Satisfaction with discrete facial areas influences self-perceptions of attractiveness, self-esteem, and quality of life. Currently, there is a lack of understanding of how the submental area impacts feelings and behaviors. OBJECTIVE: To characterize the effects of submental fat (SMF) on feelings/emotions and actions/behavior among adults in the United States. METHODS: Online health-based surveys recruited approximately 400 adults (18-65 years) in each of 5 categories based on the respondent's assessment of their SMF. Respondents either agreed or disagreed with 17 statements regarding their feelings/emotions and actions/behaviors related to the area underneath their chin. RESULTS: Overall, 1996 respondents were included (equal distribution of males/females; mean age, 41.9 years). Even a slight amount of chin fat was associated with negative feelings and behaviors. As SMF increased, so did the number of respondents reporting negative self-perceptions such as being embarrassed by the area under their chin. In general, a greater percentage of female compared with male respondents reported negative feelings and behavioral changes due to their submental area. CONCLUSION: Excess SMF can have a substantial negative effect on a person's feelings of attractiveness and behaviors. Reduction of SMF may not only improve one's appearance, but also may enhance one's self-esteem.
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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) | 0.002 | 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".