Posture-based Assessment: Thread Lift Versus High-intensity Focused Ultrasound in Asians
Bibliographic record
Abstract
Background: Minimally invasive facial laxity treatments, such as thread lifts and high-intensity focused ultrasound (HIFU), are increasingly popular in Asia, but standardized guidelines are lacking. Treatment outcomes are often subjective and heavily dependent on the physician’s expertise. Earlier studies indicate that facial soft tissue shifts significantly between supine and seated positions in cases of laxity. This study aimed to objectively evaluate facial laxity improvements after thread lift and HIFU treatments in Asians. Methods: Photographs of 143 Japanese individuals (mean age, 41.0 ± 15.5 y; mean body mass index, 21.2 ± 3.2 kg/m 2 ) taken between February and May 2024 were analyzed. Frontal and lateral facial measurements in both seated and supine positions were compared across untreated, HIFU, and thread lift groups using ImageJ software. Statistical significance was determined using t tests with a threshold value of P less than 0.05. Results: The soft tissue shifts were measured as 20.8% (SD = 19.4) in untreated, 20.7% (SD = 14.1) in HIFU, and 14.1% (SD = 11.6) in thread lift groups for frontal views. Lateral views showed shifts of 41.4% (SD = 20.9), 38.2% (SD = 20.7), and 33.4% (SD = 23.2), respectively. Significant reductions in tissue movement were observed in the thread lift group (frontal, P = 0.0092; lateral, P = 0.02) but not in the HIFU treatment group. Conclusions: Thread lifting significantly reduces facial tissue movement between seated and supine positions, indicating superior efficacy in preventing facial laxity. This study underscored the importance of assessing facial laxity in the seated position to accurately gauge treatment effects.
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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 | 0.001 |
| 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".