Commentary on: Complications from Fat Grafting and Gluteal Augmentation in Outpatient Plastic Surgery: An Analysis of American Association for Accreditation of Ambulatory Surgery Facilities (AAAASF, QUAD A) Data
Bibliographic record
Abstract
See the Original Article here. The authors of “Complications from Fat Grafting and Gluteal Augmentation in Outpatient Plastic Surgery: An Analysis of American Association for Accreditation of Ambulatory Surgery Facilities (AAAASF, QUAD A) Data” should be commended for an interesting article and for including the data from an office operating room (OR) or accrediting organization to better understand the complications after gluteal fat grafting.1 Their article acknowledges that the challenge to studying these problems is that the vast majority of these surgeries are performed in office ORs or ambulatory surgery centers that are accredited by a handful of agencies, each with their own reporting criteria. QUAD A, formerly the American Association for Accreditation of Ambulatory Surgery Facilities (AAAASF), is a well-known and respected accrediting agency that requires their facilities to report unintended sequelae through their online reporting system every quarter (the numerator in complication rates). However, QUAD A does not require their member facilities to report the total number of surgeries or the types of surgeries they perform every quarter (the denominator). This lack of total procedure counts can exaggerate complication rates and makes it difficult to calculate precise morbidity and mortality ratios. Nevertheless, QUAD A should be commended for collecting this complication data because it gives us a window into understanding the sequelae of this procedure. The authors noted that plastic surgery societies have estimated that approximately 29.7% of all fat grafting surgeries are gluteal fat grafting procedures and that approximately 20,000 to 30,000 BBLs (“Brazilian butt lifts”) are performed every year in the United States by American Board of Plastic Surgery (ABPS) certified plastic surgeons.2,3 Because the QUAD A data noted that 46,244 fat grafting cases were reported, the authors estimated that 29.7% of these or at least 13,735 BBLs were performed in QUAD A facilities over 3 years, or approximately 4578 BBLs per year. If this study accounts for 4578 annual BBLs, where did the other 15,000 to 25,000 BBLs take place? Did they occur in QUAD A facilities but were not reported? Did they take place in non–QUAD A office ORs or ambulatory surgery centers? This does not even address the unknown number of BBLs performed by non-ABPS surgeons. Among the estimated 13,735 QUAD A BBLs, the authors noted 153 complications and 4 deaths for an estimated overall complication rate of 1.11% to 2.01% and an estimated mortality rate of 0.02% (4/13,735 or 1 in 3433). And yet, these complication rates are certainly inflated, because the number of complications is divided only by the reported number of BBLs, not the total number of BBLs in QUAD A facilities. If the total number of BBLs was utilized, this ratio would inevitably be far lower and more representative. The article noted that, of the 4 deaths captured in the 2018 to 2021 US QUAD A data, 1 to 2 BBL deaths occurred after unspecified (fat or thrombus) pulmonary emboli. And yet, in the same period in South Florida alone, at least 11 BBL deaths from pulmonary fat emboli have been documented by autopsy reports.4 Did these South Florida deaths occur in non–QUAD A facilities? Are QUAD A facilities safer than other centers? Mandatory reporting of complications and procedure total numbers could highlight the enhanced safety of QUAD A facilities and their surgeons. When studying these complication ratios, reporting the numerator raises awareness and attention, but understanding the denominator gives us context and the true size of the problem. Accrediting agencies have an essential function in establishing safe environments for surgery. If agencies mandate the reporting of complications, it is in the best interest of the agency, the facility, the surgeons, and their patients to also mandate submission of the total number of procedures. Reporting an incomplete and smaller number of total procedures only inaccurately exaggerates complication rates. Accrediting agencies could use real data to highlight the safe environments of their members. Surgeons and facilities would seek to associate with agencies with verified low complication rates and patients would gravitate to these surgeons and centers for safer surgery. Dr Pazmiño is a consultant for Clarius (Vancouver, BC, Canada). The author received no financial support for the research, authorship, and publication of this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".