Bibliographic record
Abstract
This paper presents an extensive literature review of the psychology of facelift patients as it has evolved over the past 50 years. Earlier studies revealed significant levels of pre and perioperative depression. Facelift patients generally exhibit emotional and social concerns about facial appearance that are higher than the general population. Many are undergoing midlife situational stresses and may lack the positive characteristics to deal with them. The most common diagnoses seen include depression, impulsivity, unstable personality, and passive dependence, albeit not necessarily serious. Improvement in body image is the major driver for surgery. Characteristics of female patients as defined by their age are described. These include the younger emotionally dependent group, the worker group of middle age, and the older grief group. Male patients are seen to have a higher level of psychological dysfunction, but a higher improvement in postoperative quality of life. Motivations for surgery include increasing self-esteem, making new friends, improving relationships, and getting better jobs. Overall patient satisfaction is more than 95%, with improvement seen in positive changes in their life, increased self-confidence and self-esteem, decreased self-consciousness about their appearance, and overall improvement in quality of life. Postoperative psychological reactions are seen in about half the patients, these primarily being anxiety and depression of varying degrees. Predictors of patient satisfaction include the desire for self-image improvement in contradistinction to a change in life situation. Negative predictors include male sex, young age, unrealistic expectations, relationship disturbances, and preexisting psychological pathology. The importance of good patient selection in achieving a satisfied patient is outlined and emphasized.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".