Developing Consensus-Based Guidelines for Case Reporting in Aesthetic Medicine: Enhancing Transparency and Standardization
Bibliographic record
Abstract
Clinical case reporting plays a vital role in sharing detailed patient narratives, providing insights into rare conditions, innovative treatments, and unexpected outcomes. However, existing reporting guidelines in aesthetic medicine fail to capture the specific nuances of procedures and outcomes in this field. The authors' objectives were to develop comprehensive guidelines for Case REporting in Aesthetic Medicine (CREAM). The study employed a 3-phase consensus process, including a literature review, expert interviews, and a consensus meeting. A diverse group of 10 expert participants (plastic surgeons, dermatologists, noncore specialists, evidence-based medicine expert, and research scientist) in Phase I and 30 experienced aesthetic practitioners in Phase II contributed to the research. Statistical analysis was conducted to assess agreement levels among participants and explore associations and variations within the data. The participants represented various specialties, genders, LGBTQ+ identities, and ethnic backgrounds. The research resulted in the development of the CREAM guidelines, consisting of a 16-item checklist. The guidelines covered essential aspects of case reporting, such as patient and practice information, procedure details, clinical assessment and outcomes, adverse events, and ethical considerations. Statistical analysis indicated a high level of consensus among participants, as well as significant associations between checklist items. CREAM guidelines represent a step toward enhancing transparency and standardization in case reporting in aesthetic medicine. Adhering to these guidelines will allow authors to contribute to a robust evidence base, prioritize patient safety, and drive advancements aesthetic medicine.
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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 | 0.639 | 0.738 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.025 | 0.012 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.013 | 0.023 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".