Comparison of Single- Versus Dual-Vector Technique Using Facial Suspension Threads: A Cadaveric Study Using Skin Vector Displacement Analysis
Bibliographic record
Abstract
BACKGROUND: Facial suspension threads have been successfully used for facial soft-tissue repositioning. When using facial suspension threads, it is unclear which technique and/or material has the greatest lifting effect for the middle and lower face or which technique/material best reduces the appearance of the jowls. MATERIAL AND METHODS: Three female and 2 male cephalic specimens of Caucasian ethnicity (65.2 ± 8.3 years; 20.72 ± 2.6 kg/m) were analyzed in an upright secured position. Polydioxanone and polycaprolactone bidirectional barbed facial suspension threads were introduced by an 18 G, 100 mm cannula. The single-vector technique aimed toward the labiomandibular sulcus, and the dual-vector technique aimed toward the labiomandibular sulcus and the mandibular angle. Computation of vertical lifting, horizontal lifting, and volume reduction at the jowls and along the jawline were calculated using 3D imaging. RESULTS: The dual-vector technique effected a greater vertical lifting effect (4.45 ± 2.78 mm vs 2.99 ± 2.23 mm) but a reduced horizontal lifting effect (0.33 ± 1.34 mm vs 0.49 ± 1.32 mm). The dual-vector technique effected less volume reduction at the jowls 0.32 ± 0.24 cc versus 0.41 ± 0.46 cc and less volume reduction along the jawline 0.46 ± 0.48 cc versus 0.87 ± 0.53 cc (dual-vector vs single-vector). CONCLUSION: This study provides evidence resulting from cadaveric observations for the overall nonsuperiority of the dual-vector technique compared with the single-vector technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".