Prospective evaluation of incobotulinumtoxinA in the management of the masseter using two different injection techniques
Bibliographic record
Abstract
BACKGROUND: ) has been used previously in the management of masseteric hypertrophy. However, a standardized injection technique has not been established. The goal of the present study was to evaluate the efficacy and safety of two injection techniques in the management of masseteric hypertrophy using incobotulinumtoxinA. METHODS: Thirty female patients with masseteric hypertrophy were recruited and evenly randomized to receive bilateral treatments of either a single-injection technique (SIT) or a multiinjection technique (MIT). Improvement of masseteric hypertrophy was assessed at week 16 using standardized measurements and photographs. Patients completed a 5-point satisfaction questionnaire while physicians completed the Global Aesthetic Improvement Scale (GAIS) and 10-point photonumeric masseter prominence rating scale. RESULTS: There were no significant differences in physician ratings on the photonumeric scale and GAIS between the SIT and MIT groups. Results of the standardized measurements also revealed no significant difference between injection techniques. Majority of patients at every visit reported being "satisfied" with treatment results. Clinically, the number and severity of adverse events were similar between groups. CONCLUSION: This study supports the noninferiority of both SIT and MIT with regard to efficacy and safety in the management of masseteric hypertrophy, using incobotulinumtoxinA.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".