Treatment of crow’s feet lines and forehead lines with Botox (onabotulinumtoxinA): Development, insights, and impact
Bibliographic record
Abstract
Extrinsic and age-related intrinsic factors contribute to the development of facial lines, including lateral canthal lines (called crow's feet lines [CFL]) and horizontal forehead lines (FHL). OnabotulinumtoxinA is a highly effective treatment for facial lines that inhibits acetylcholine release at the neuromuscular junction. This temporary chemical denervation leads to localized muscle relaxation and subsequent wrinkle reduction. Early studies of onabotulinumtoxinA treatment for facial neuronal disorders such as dystonia documented improvements in FHL and CFL. After the neurotoxin was approved for treating frown lines (glabellar lines [GL]), individuals requested treatment for other rhytids, and physicians continued assessing use in new areas. Once onabotulinumtoxinA was in clinical trial development, its efficacy and safety for CFL and FHL were successively evaluated as required by the US Food and Drug Administration and by key global health authorities, including those in the European Union, Japan, and China. Allergan, collaborating with leading physicians, established clinical programs that included novel safety and efficacy measures to meet regulatory requirements. Global, phase 3, randomized, controlled studies of CFL and FHL met rigorous primary endpoints. Some countries mandated clinical trial data beyond US and European regulations, and Allergan conducted 11 studies in total, fulfilling diverse regulatory and study population data requirements. Adverse events associated with local spread, including brow and eyelid ptosis, diplopia, headache, and eyelid sensory disorder, were infrequent and well tolerated. Consequently, onabotulinumtoxinA treatment of upper facial lines is now established globally as a highly effective, minimally invasive treatment for patients to achieve a natural appearance and look younger.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".