Temporal volume increase after reduction of masseteric hypertrophy utilizing incobotulinumtoxin type A
Bibliographic record
Abstract
BACKGROUND: Treating the lower face with neuromodulators and targeting the masseter muscle can reduce masseteric hypertrophy but can also change the facial shape. A novel observation after the treatment of the masseter muscle with incobotulinumtoxin Type A was the increase in temporal volume. AIM: Objectively assess temporal volume increase following treatment of masseteric hypertrophy using incobotulinumtoxin Type A. METHODS: Nine female patients with a mean age of 35.11 years ± 9.1 [Asian (11.1%) and Caucasian (88.9%)] were treated with incobotulinumtoxin Type A for masseteric hypertrophy. Masseteric prominence and temporal volume were assessed by two independent raters, and temporal fossa volume was measured via 3-dimensional volumetric imaging. RESULTS: Independent of the neuromodulator injection technique (ie, single-injection versus multi-injection), a reduction in masseteric hypertrophy occurred represented by a decrease in the masseter prominence scale. In addition, the treatment resulted in a significant improvement of the temporal volume scale and an increase in the measured volume of the temporal fossa. None of the presented measurements were statistically significantly different between the two utilized injection techniques. CONCLUSIONS: This study supports using a full-face approach when performing aesthetic treatments. Anatomical concepts can help to guide treatments: the compensatory increase in temporalis function after masseter muscle treatment resulted in an increased in temporal fossa volume. The findings presented herein should not be considered as a new concept for treating the temporal fossa but rather as an additional possibility for increasing the temporal volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".