Bibliographic record
Abstract
The demand for non-surgical facial rejuvenation procedures is rising, and they are more popular than ever, with the aesthetic uses of botulinum toxin dramatically changing the landscape of facial rejuvenation. Botulinum toxin is a neurotoxin that works within cholinergic synapses present at neuromuscular endplates, preventing the transmission of neurotransmitters, such as acetylcholine, from nerves to muscles. This interference with nerve impulses leads to the muscles being temporarily weakened (paralysis). Botulinum toxin A was approved by the US Food and Drug Administration (FDA) for use in the glabella in 2002, followed by crow's feet in 2013 and then the forehead in 2017, with other aesthetic uses being classed as off-license. Botulinum toxin A yields good results in carefully selected patients, and a thorough consultation should always take place. Consultations should include management of expectations and the explanation that botulinum toxin A works on dynamic lines, rather than static lines. Treatment areas can be split into the upper face (glabellar, transverse forehead lines and lateral orbicularis oculi); mid face (bunny lines and perioral vertical lip lines); and lower face (masseter hypertrophy, mentalis, platysmal bands and gummy smile). Each patient should be assessed individually to determine individual anatomy, including the size, strength and location of muscles, with doses being adjusted accordingly.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".