Introduction to the EBM Hub in the Aesthetic Surgery Journal
Bibliographic record
Abstract
Although it was unfamiliar to most plastic surgeons just a few years ago, evidence-based medicine (EBM) is not only becoming more and more visible in our journals and meetings, but it is also beginning to insert itself into our daily discussions with colleagues. We see the EBM levels of evidence ratings in our journals and we notice the declarations of the level of evidence in presentations at our meetings. Descriptions of study methodologies in our journals are becoming more complex, with the inclusion of power analyses, confidence intervals, forest plots, and other epidemiologic and statistical terms that we may not fully understand. We see several different statistical analysis techniques—t-test, χ2-test, analysis of variance, regression analysis, Spearman's rank correlation coefficients, etc—yet most of us may not know whether the right statistical tool was used for a particular study, and more importantly, what the results really mean. Were patients appropriately randomized, were the outcome metrics validated, and what biases may have affected the conclusions? It is critical that we understand methodologies and answer questions like these because the whole goal of EBM is to choose the best available evidence and apply it to treatment of our patients. Appraising a published article for inherent weaknesses, strengths, flaws, and biases—in other words, checking its validity—is not an easy task. Most of us become intimidated by the statistical terminology, but this need not be the case. Adopting EBM into your practice does not require you to obtain a master's degree in statistics or epidemiology, nor does it mean that you have to spend hundreds of hours poring over complex articles and book chapters. It also does not require you to master new statistical analysis software programs. There are experts who can help with all of those aspects. However, learning the basic principles of appraisal can be simple and rewarding. You can then decide if the article you read has a high or low level of evidence. Applying such basic knowledge will afford you extra confidence in your interpretations and patient-care decisions. As you learn these principles, you can also educate your patients so that they too can make more informed decisions. That is the goal of the EBM Hub series in the Aesthetic Surgery Journal—to provide you with quick, straightforward, and practical EBM guidance that you can use right then and there as you read articles within that issue of the Journal. When in a written format, the installments will be short—generally 1 page or less—so they will take only a few minutes of your time to read and will focus on a pertinent big-picture point. We will also make good use of the Aesthetic Surgery Journal website and RADAR application to publish short, straightforward videos that run though the thought process for considering how EBM might affect your own interpretation of a particular article. To make this even more relevant, we will use current articles as examples of the EBM principles at work. Feel free to write to me (feaves@emory.edu) upon submitting a new manuscript to the Journal if you think your work could serve as a role model in this context. In addition, although on its surface EBM sounds anything but interesting and fun, we are committed to making these installments just that. It can feel great to know that you really understood an article instead of solely relying on the authors' interpretations and conclusions. Maybe you'll find that an article doesn't provide enough evidence for you to change your technique, maybe you'll find that it does, or maybe you will feel that the jury is still out on the topic and that you'll stick with your current techniques until more information comes along. Of course feeling the power of greater understanding and knowledge is wonderful, but that is only part of the goal of this series. We also want to provide encouragement for you to make significant changes in your practice as guided by the best evidence. Change, of course, is threatening and hard, and we humans naturally resist it. As aptly noted by Charles Kettering, “The world hates change, yet it is the only thing that has brought progress.”1 It is easy for us to stay within our comfort zones and continue to treat patients as we have for decades, often using the methods that we learned in residency. Sometimes, of course, those tried-and-true principles are supported by the best current evidence and should be continued. Sometimes, however, they are not. After all, putting spoons in a hole in the cranium to stir up the brain and let out evil spirits (trepanation) was once standard practice (“it's how I do it”), as were bloodletting, mercury treatment for wounds, and urine therapy. Perhaps you've concluded that the current best evidence suggests that routine use of surgical drains in breast reduction is not indicated; however, it is hard for so many to take that first step and leave them out (“maybe next case,” you tell yourself). Similarly, it is hard to change our practices concerning prolonged antibiotic use, prophylaxis for venous thromboembolic events, or implementation of system and process improvement. The EBM Hub will provide valuable guidance with regard to strategies to implement evidence-based changes in your practice and will enable you to positively engage your partners, staff, and patients in the process. Becoming an evidence-based aesthetic surgeon doesn't require that you change the world in a day. Slow, steady, measured, practical, and thoughtful incremental changes—a little here, a little there—will transform your practice over time into one that is on the cutting edge of the best evidence-based practices and will showcase your excellent skills as a surgeon. Please note the EBM Hub icon at the top of the article; this icon will appear on all future EBM Hub articles and any related articles that are referenced, allowing you to quickly thumb to the correct page in the Journal. Please also watch for EBM Hub videos that will supplement the print edition. They can be found at the ASJ YouTube channel here: www.youtube.com/user/ASJOnline. Once again, we welcome you to the EBM Hub. We look forward to sharing this educational journey and to learning together along the way! The authors have no conflict of interests to disclose related to the content of this article.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".