Bibliographic record
Abstract
LEARNING OBJECTIVES: After reading this article, the participant should be able to: 1. Identify and describe the anatomy of and changes to the aging face, including changes in bone mass and structure and changes to the skin, tissue, and muscles. 2. Assess each individual's unique anatomy before embarking on face-lift surgery and incorporate various surgical techniques, including fat grafting and other corrective procedures in addition to shifting existing fat to a higher position on the face, into discussions with patients. 3. Identify risk factors and potential complications in prospective patients. 4. Describe the benefits and risks of various techniques. SUMMARY: The ability to surgically rejuvenate the aging face has progressed in parallel with plastic surgeons' understanding of facial anatomy. In turn, a more clear explanation now exists for the visible changes seen in the aging face. This article and its associated video content review the current understanding of facial anatomy as it relates to facial aging. The standard face-lift techniques are explained and their various features, both good and bad, are reviewed. The objective is for surgeons to make a better aesthetic diagnosis before embarking on face-lift surgery, and to have the ability to use the appropriate technique depending on the clinical situation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.306 | 0.113 |
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".