Impact of Glabellar Injection Technique With DaxibotulinumtoxinA for Injection on Brow Position
Bibliographic record
Abstract
BACKGROUND: Precise injection technique is vital for avoiding suboptimal eyebrow position when treating glabellar lines with botulinum toxin type A. OBJECTIVES: The aim of this study was to evaluate the impact of glabellar injection technique on eyebrow position in patients treated with DaxibotulinumtoxinA for Injection (DAXI). METHODS: This retrospective post hoc analysis involved 60 adults who received a single treatment with DAXI 40 U to the glabella and had standardized facial photography. Median vertical and horizontal displacement of the brows (at rest) at baseline and 2 weeks after glabellar injection were measured. Brow position outcomes were evaluated by an oculoplastic surgeon and expert anatomist. Investigators were interviewed to ascertain individual injection techniques. RESULTS: Precise injection location and depth, and median resting brow position following treatment varied between investigators. Positive brow outcomes were achieved with deep DAXI injections into the medial corrugator, superficial lateral corrugator injections placed between the midpupil and lateral limbus, and deep midline procerus injections. Glabellar injection technique that more precisely targeted the corrugator muscles resulted in longer glabellar line treatment duration compared to a less targeted technique. Medial corrugator injections above the medial brow; lateral corrugator injections administered deeply or more medially, toward the medial third of the brow; and procerus injections superior to the inferomedial brow tended to be associated with suboptimal outcomes that were more apparent during dynamic expression. CONCLUSIONS: Aesthetically pleasing brow outcomes and greater duration of efficacy can be achieved with an injection pattern that precisely treats the anatomic location of the corrugator supercilii and procerus muscles, avoiding the frontalis.
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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 | 0.002 |
| 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) | 0.001 | 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".