The role of toxins and fillers in perioral rejuvenation
Bibliographic record
Abstract
Dr. Barton: The first patient is a 40-year-old woman who is looking for options to increase the fullness in her lips (Figure 1). Dr. Coleman, what would you suggest? Dr. Coleman: Her lips are not as easy to improve as atrophic lips in which you simply fill in the fat, like filling an envelope. Here, the structure of the lip actually has to change. First, I would focus on flipping out the lip. Getting the vermilion to evert would be the primary focus. I would place fat under the vermilion, primarily, and the mucosa, secondarily, paying attention to eversion. Second, I would create a clear white roll by intradermal fat placement. Third, I would focus on lower lip volume enhancement more than upper lip. I think it is a common error to focus on the upper lip and forget about the lower lip; if you do not get eversion of the lower lip, you will have a very funny-looking lip.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".