Optimizing Revenue At a Cosmetic Surgery Centre
Bibliographic record
Abstract
BACKGROUND: The demand for cosmetic surgery and services has diminished with recent fluctuations in the economy. To stay ahead, surgeons must appreciate and attend to the fiscal challenges of private practice. A key component of practice economics is knowledge of the common methods of payment. OBJECTIVE: To review methods of payment in a five-surgeon group practice in central Texas, USA. METHODS: A retrospective chart review of the financial records of a cosmetic surgery centre in Texas was conducted. Data were collected for the five-year period from 2003 to 2008, and included the method of payment, the item purchased (product, service or surgery) and the dollar amount. RESULTS: More than 11,000 transactions were reviewed. The most common method of payment used for products and services was credit card, followed by check and cash. For procedures, the most common form of payment was personal check, followed by credit card and financing. Of the credit card purchases for both products and procedures, an overwhelming majority of patients (more than 75%) used either Visa (Visa Inc, USA) or MasterCard (MasterCard Worldwide, USA). If the amount of the individual transaction surpassed US$1,000, the most common method of payment transitioned from credit card to personal check. CONCLUSIONS: In an effort to maximize revenue, surgeons should consider limiting the credit cards accepted by the practice and encourage payment through personal check.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".