The Power of Combined Therapies: BOTOX and Ablative Facial Laser Resurfacing
Bibliographic record
Abstract
Introduction: The etiology of facial rhytides is multifactorial. Static rhytides are caused by environmental factors and heredity, whereas dynamic rhytides are caused by the repetitive action of muscles during facial expressions. A successful multifactorial therapeutic approach is to use ablative laser resurfacing to reduce the static component and BOTOX to soften the dynamic component of the facial rhytide. Materials and Methods: We retrospectively analyzed the charts and photographs of 53 consecutively treated subjects to evaluate the relative strengths of the static and dynamic treatment modalities on the individual's final aesthetic result. To remove any potential for bias, the treatments were all carried out by author J.C. The photographic and chart reviews were performed by author A.C., and the subjective patient questionnaire was completed by author A.Z. Results: Adding BOTOX to CO 2 laser resurfacing improved the result objectively from 30% to 60%. However, adding BOTOX to Erbium:YAG laser resurfacing improved the result only marginally (40% to 47%). Discussion: Combined therapy with CO 2 laser resurfacing and BOTOX gave the most pronounced aesthetic benefit. Combined treatments with Erbium-YAG laser resurfacing and BOTOX was a less powerful treatment blend.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".