Objective analysis of the effectiveness of facial massage using breakthrough computed tomographic technology: A preliminary pilot study
Bibliographic record
Abstract
BACKGROUND: Facial massage is empirically known to be associated with morphological changes, such as improvements in facial sagging. However, quantified objective evaluations of massage-induced changes have not been performed to date. This preliminary pilot study aimed to verify the effectiveness of facial massages by using breakthrough computed tomographic technology. MATERIALS AND METHODS: Five healthy adult volunteers (three women and two men; age, 29-37 years) were enrolled, and computed tomography (CT) examinations using a 320 detectors-spiral CT system known as 320-multidetector-row CT (MDCT) were performed before and after facial massages. Each participant performed a self-massage twice daily for 2 weeks. Massage-induced changes in the cheeks and the superficial musculoaponeurotic system (SMAS) were analyzed by two radiologists on a workstation with a high-accuracy imaging analysis system. RESULTS: After facial massage, the malar top became thinner by -0.8% ± 0.45% and shifted cranially and horizontally over a distance of 3.9 ± 1.94 mm. The SMAS-height, defined as the highest vertical distance of the SMAS, increased by 2.6% ± 2.6%. The change rate in cheek thickness and SMAS-height showed a significant correlation (r = -0.63; P < 0.05). These changes were attributed to the lifting and tightening effects of facial massage. CONCLUSION: We conducted a detailed analysis of the effects of facial massages by using the breakthrough CT technology. Our results provide useful information for beauty treatments and could contribute to the collection of objective scientific evidence for facial massages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".