Psychological Screening Measures for Cosmetic Plastic Surgery Patients: A Systematic Review
Bibliographic record
Abstract
With the increasing popularity of cosmetic surgery procedures, preoperative psychological assessment of cosmetic surgery patients may improve outcomes by highlighting patient expectations and motivations, as well as by identifying those who may require psychological referral. In this article, the authors describe a systematic literature review to identify and evaluate current self-report tools used in the psychological screening of cosmetic surgery patients prior to surgery. Articles related to the preoperative mental health assessment of cosmetic surgery patients were identified by searching MEDLINE, EMBASE, HAPI, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials through November 2010. The full text of potentially relevant articles was examined by 2 reviewers, and articles that met the inclusion criteria were reported. Close reading of 100 full-text articles showed that although a variety of instruments are currently being used as preoperative assessment tools, there are limitations to their validity and usefulness in the screening of cosmetic surgery patients. To properly assess cosmetic surgery patients, a scientifically sound and clinically useful instrument is needed.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".