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 …
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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 | 0.027 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| 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".