Determinants and Timeline of Perioperative Anxiety in Mohs Surgery
Bibliographic record
Abstract
BACKGROUND: Patients undergoing Mohs micrographic surgery (MMS) exhibit anxiety relating to cancer cure or the expected cosmetic outcome. OBJECTIVE: To obtain quantitative measurements of perioperative cancer and cosmetic anxiety levels in first-time MMS patients. Parameters influencing anxiety and its natural course were assessed. METHODS: Prospective, single-blinded, questionnaire study of 173 patients undergoing MMS of the face. Anxiety levels were assessed using a visual analog scale preoperatively and postoperatively over 6 months. RESULTS: Mohs patients demonstrate a trend to greater or equal anxiety about cancer over cosmesis at all measured time points, but differences only reached statistical significance beginning 1 week postoperatively. Clinically relevant lowering of cancer anxiety levels is delayed until 3 months postoperatively. Cosmetic anxiety reaches a clinically relevant improvement by 1 week. The intuitive predictors of cosmetic anxiety, namely female gender and younger age, were quantitatively reinforced in this study. The predictor of cancer anxiety was the use of preoperative lorazepam. CONCLUSION: To maximize patient care, Mohs surgeons must be aware of covert patient anxieties and the parameters, which influence these anxieties. Identifying and anticipating the course of cancer- and cosmetic-related anxieties will reduce patient fears, improving their satisfaction with the MMS experience.
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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 | 0.000 | 0.003 |
| 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) | 0.001 | 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".