Bibliographic record
Abstract
Introduction: The objectives of this article are to (1) evaluate the popular fillers as practice builders and (2) outline a typical practice builder program for the surgeon. Methods: Information was gained from a search of the literature, 25 years of clinical experience, and interviews as well as a survey of 35 physicians to determine present surgeon techniques. Fillers were graded 1–5 to qualify as an income generator (with 5 being the highest). Results: Autologous fat scores were higher than that of the other fillers, although any filler could be in a practice-builder program. All bioresorbable fillers will need additional fill to obtain the desired results, but fat has an added benefit of some eventual permanence. When more than 5 mL of filler is needed, fat has also has the most reasonable cost. The commercial fillers will cost $93 to $200 per milliliter. Autologous fat, even with the added cost of anesthesia, disposables, and a minor operating room setup, will be more economical for the patient in amounts greater than 5 mL. When the surgeon can charge less for the filler itself, there is more room for a fair professional fee. There are 3 instrument systems available for fat transfer on the market. These systems accomplish different goals; thus, all have their position in the marketplace. These systems include the TissuTrans, the LipiVage, and the Coleman type modified liposuction instruments. The latter includes all micro-cannula-type instruments. Since it has been established that fat trauma during transfer should be minimized, the system and technique that will give the least amount of trauma to the fat cell should be the first choice. Conclusions: There is now a resurgence of interest in autologous fat because of the very high costs of commercial substances. Because of public acceptance and the availability and cost of economical raw goods, autologous fat can be a key filler in a practice-builder program. To have a successful goal-oriented program, one must use lipocyte-friendly instruments and put a great deal of effort into training the staff. The program must be organized, advertised, marketed, and reviewed at specific intervals for improvement. NOTE: When speaking of the best practice-builder filler, it is assumed that the surgeon will follow ethical guidelines in choosing the best filler for the patient, not just the filler that fits his or her program.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.074 | 0.026 |
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".