Reply: What Is Driving Paradigm Shifts in Plastic Surgery and Is Cosmetic Surgery Keeping Up?
Bibliographic record
Abstract
Sir: We thank these esteemed colleagues for taking the time to read and comment on our article1 and congratulate them for their investment, commitment, and growth of the Aesthetic Surgery Journal. They make numerous interesting comments that inspire discussion, for which we are grateful to have the opportunity to respond to herein. Our purpose was to contrast plastic surgery cosmetic market share/volume with innovation using citation volumes (not impact factor), as a proxy of sorts. Although we make reference to Plastic and Reconstructive Surgery’s impact factor in our article, we fully agree that there are inherent limitations to impact factor, and thus focused our attention at raw citation volumes. Regarding the query of a 50-year time horizon for this study and exclusion of basic science articles, although this was an arbitrary time horizon, we wanted to ensure we captured as many of the revolutionary articles as we could over time. We emulated the methodology of other authors in this regard.2 In fact, we certainly missed some transformational articles because of our methodology, including that of Murray’s landmark renal transplant work.3 Many basic science articles would make our list; nonetheless, we wanted to focus on clinical articles, because other authors have previously published citation analyses incorporating both clinical and basic science articles.4 To respond to the question of changes/advances over time, we were mindful to perform a decade-by-decade comparison as noted in Table 1 of our article, and do acknowledge that more cosmetic articles have been published in the past 15 years. Furthermore, it has been described by Joyce5 that the first 16 years is the interval with the most citations following publication. To this end, almost half of the cosmetic articles in our top 100 compendium were published since 2000, which indeed offers optimism toward an increased growth trajectory of cosmetic research. To the question of our hypothesis wherein a proportional market share should correspond to proportional innovation, we decided to include only “surgical” volumes; however, if we were to include minimally invasive volumes (which represent many advances captured in the areas of cosmetic innovation), also included in the American Society of Plastic Surgeons National Plastic Surgery Statistics Report,6 the percentage of “cosmetic” volume (surgical plus minimally invasive) is not simply 24 percent, but rather 75 percent, which would alter our assumptions considerably. In sum, we as academic surgeons will know full well that much of this foundational work (basic science and clinical) is done by a network of research coordinators, medical students, resident trainees, and master’s/doctoral students. Although this type of infrastructure rarely exists in most cosmetic practices (yet may exist within industry), would there not be the potential for exponential cosmetic growth and innovation with this type of system in place? This network could grow grant funding, registries, multicenter trials, research clusters, partnerships with industry, even cosmetic incubators that may spin off start-ups and the like, thereby increasing representation in academic cosmetic research and development, in turn leading to new innovations, which ultimately will offer increased marginal benefit for our patients. DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. Marija Bucevska, M.D.Jugpal S. Arneja, M.D., M.B.A.University of British ColumbiaVancouver, British Columbia, Canada
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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 | 0.009 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.027 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".