Retention Rates Among Patients Undergoing Multimodal Facial Rejuvenation Treatment Versus a Single Monotherapy in Cosmetic Dermatology Practices
Bibliographic record
Abstract
BACKGROUND: Facial aging is a multifactorial process. Accordingly, expert opinion has largely been unanimous in that multimodal treatment targeting various aspects of the aging face provides superior results. However, there is a lack of studies exploring patient response. OBJECTIVE: To compare patient retention between triple multimodal facial rejuvenation treatment (neuromodulator, filler, and energy-based therapy) and monotherapy (neuromodulator alone). METHODS: A retrospective, multicenter (the United States, Canada, and Germany) study was performed. Cases were retrieved from July 2015 to June 2016. The study compared patients who had undergone monotherapy (neuromodulator), combined multimodal treatment (neuromodulator, filler, and energy-based therapy on the same day), and sequential multimodal treatment (neuromodulator, filler, and energy-based therapy over a 1-year period). Retention rates were calculated. RESULTS: A total of 509 patients were included: monotherapy (300), sequential multimodal treatment (93), and combined multimodal treatment (116). Patient retention was significantly higher in the combined multimodal treatment group compared with the monotherapy and sequential multimodal treatment groups (p < .001). Subgroup analysis revealed similar trends at all sites. CONCLUSION: Based on retention rates, patients are more likely to return to the clinic when multiple treatment modalities are used during 1 encounter. These data further solidify the importance of multimodal therapy for both the provider and the patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".