Analysis of the efficacy of marketing tools in facial plastic surgery.
Bibliographic record
Abstract
OBJECTIVES: To compare referral sources to a facial plastic surgery practice and to develop models correlating the referral source with the decision for surgery. DESIGN: Retrospective descriptive study. SETTING: Well-established, metropolitan, private facial plastic surgery practice with training fellowship affiliated with an academic centre. METHODS: One-thousand eighty-nine new consecutive patients presenting between January 2001 and December 2005 recorded intake data including age, gender, and chief complaint. Final data input was their decision for or against surgery. MAIN OUTCOME MEASURES: Main outcome measures included differences in referral sources based on data collected and how those sources related to decision for surgery. RESULTS: A 50% conversion rate was found. Women and older patients were more likely to be referred from magazines, television, and newspapers and for facial rejuvenation. Men and younger patients were more likely to be referred from the website and for rhinoplasty. For facial rejuvenation, both the number of patients interested in and the probability that they agreed to the procedure increased with age. For rhinoplasty, the converse was true. The most likely patients to schedule surgery were those who were referred from other patients, friends, or family members in our practice. CONCLUSIONS: The data confirm that word-of-mouth referrals are the most important source for predicting which patients will elect to proceed with surgery in this established facial cosmetic surgery practice.
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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 | 0.024 | 0.182 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".