Methodological Guide to Adopting New Aesthetic Surgical Innovations
Bibliographic record
Abstract
Aesthetic surgery is known for its prolific introduction of new techniques, devices, and products. The implementation of any aesthetic innovation, however, may inadvertently expose patients to potential complications and adverse events. How do we decide whether a new technique or technology is superior-in both safety and effectiveness-compared with prevailing interventions? In this paper, we present some basic steps anchored in evidence-based surgery that aesthetic surgeons need to pursue in the adoption of a new technique, technology, or product. These steps include: (1) gaining familiarity with and understanding the levels of evidence; (2) performing an effective literature search; (3) formulating a critical appraisal of an article; (4) making the decision to adopt or reject; (5) recognizing the need for continued assessment; (6) acknowledging the need for education and credentialing; and (7) translation of the gathered knowledge. We hope that this paper will foster critical thinking and reduce the reliance on "photographic evidence" in aesthetic surgery literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.225 | 0.391 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".