Developing Consensus-Based Guidelines for Case Reporting in Aesthetic Medicine: Enhancing Transparency and Standardization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it