Development of “Core Syllabus” for Facial Anatomy Teaching to Aesthetic Physicians: A Delphi Consensus
Bibliographic record
Abstract
BACKGROUND: A detailed understanding of facial anatomy, specifically the vascular framework, is crucial for delivering safe nonsurgical aesthetic procedures. To date, there is no core document based on consensus for the teaching of facial anatomy to aesthetic physicians exists. The aim of this study was to ascertain the most critical anatomical structures for avoiding disastrous complications during nonsurgical aesthetic procedures. METHODS: After a detailed literature review, Delphi questionnaire was developed listing 154 anatomical structures of the face and neck for consensus review. Thirty-five international experts in surgical and nonsurgical facial aesthetics were invited to complete an online survey designed to rank the relevance of each anatomical element on a Likert scale from 1 (not at all important) to 4 (very important) or 5 (no opinion). Consensus for items included in the core syllabus was predefined as achieving a Cronbach's α ≥ 0.90 and an agreement score of ≥ 80%. RESULTS: Thirty-four international experts (97.14%) completed the iterative online Delphi survey. The consensus among the specialists polled was achieved after 1 round of the survey (Cronbach's α = 0.94). The expert panel reached an agreement score of ≥ 80% on 137 of the 154 anatomical structures listed. CONCLUSION: The outcome of this Delphi study represents an essential first step in systematizing an evidence-based facial anatomy "Core Syllabus" for the teaching of aesthetic physicians and practitioners. This "Core Syllabus" will act as a blueprint for the educators while crafting a program.
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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 | 0.150 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".