Enhanced patient retention after combination vs single modality treatment using hyaluronic acid filler and neuromodulator: A multicenter, retrospective review by The Flame Group
Bibliographic record
Abstract
BACKGROUND: It is believed that combination treatment with both neuromodulators (NM) and hyaluronic acid soft-tissue fillers (HA) results in superior aesthetic results and increased patient satisfaction compared to either treatment alone. AIMS: To determine whether combined treatment with NM and HA leads to greater patient retention than treatment with NM or HA alone. PATIENTS/METHODS: This is a large, multi-center, retrospective review of patient retention rates from 7 aesthetic practices across 5 continents, incorporating over 2600 patients. Patient retention is interpreted as a surrogate marker for overall patient satisfaction. Retention rates were compared at 1, 3, and 5 years for patients who received NM only, HA only, or combination treatment with both NM and HA. RESULTS: Combination therapy significantly increased the probability of retention for each time point (1, 3, and 5 years) compared with NM alone (P < .0001) and HA alone (P < .0001). CONCLUSION: This large multicenter, global retrospective review demonstrates that patients who received combined HA and NM were more likely to be retained in the same practice over many years than those who received either treatment alone.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".