Novel Treatment Protocol for Combined Tear Trough Ligament Stretching and Injection
Bibliographic record
Abstract
BACKGROUND: Complex anatomical changes have been the main challenges for optimal treatment results of tear trough deformities through hyaluronic acid (HA) injections. The authors present a novel technique consisting of a preinjection tear trough ligament stretching (TTLS-I) leading to its release, and compared its efficacy, safety, and patient satisfaction to tear trough deformity injection (TTDI). METHODS: This was a 4-year, retrospective, single-center cohort study of 83 TTLS-I patients, with a follow-up period of 1 year. One hundred thirty-five TTDI patients served as a comparison group. Outcome analyses included the analysis of possible risk factors for adverse outcome and comparative statistics between the complication and satisfaction rates of the two groups. RESULTS: TTLS-I patients received significantly less HA (0.3 cc; range, 0.2 to 0.3 cc) than TTDI patients did (0.6 cc; range, 0.6 to 0.8 cc; P < 0.001). The injected HA amount was a significant predictive factor for complications ( P < 0.05). Complication rates assessed during the follow-up visit for hematomas, edema, and the need for corrective hyaluronidase injection were low in both groups, with no significant differences between the groups. TTDI patients had significantly higher rates (5.1%) of lump surface irregularities during follow-up, compared with 0% in the TTLS-I group ( P < 0.05). After 1 year of follow-up, 98.8% of TTLS-I patients were satisfied, whereas 95.6% of TTDI patients were satisfied, with no significant difference between groups. CONCLUSIONS: TTLS-I is a novel, safe, and effective treatment method, necessitating significantly less HA compared with TTDI. Moreover, it leads to very high satisfaction rates and very low complication rates. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".