Bibliographic record
Abstract
This chapter book begins with an overall discussion of the field of aesthetic medicine, with a focus on soft tissue injectables [e.g., hyaluronic acid (HA) fillers]. In particular, this book evaluates adverse events (AEs) associated with the use of aesthetic injectables. Following a general introduction to the topics of interest (Chapter 1), Chapter 2 summarizes a 53-year retrospective analysis of MedEffectTM, Health Canada’s AE reporting database. Incidence rates of AEs associated with different injectable products (e.g., HA versus neurotoxins) are calculated, using data from this review. Chapter 3 focuses on an in-depth discussion of the findings presented in Chapter 2 and develops next steps in the investigation. In Chapter 4, a thorough systematic review of the literature is reviewed for methods of preventing, managing, and treating AEs. All recommendations are graded on a scale from very low to high, based on the quality of their supporting evidence, and resulting models are developed for use in clinical practice. However, it is concluded that the majority (> 85%) of recommendations proposed to date are of very low to low (GRADE D or C) quality, relying solely on expert opinion or studies with severe limitations, and often lack direct evidence. This chapter ends with a call-to-action, encouraging investigators to develop evidence-based AE prevention, management, and treatment strategies. As an early means of responding to this call-to-action, a Safety Task Force (STF) is developed and described in Chapter 5. A STF meeting was held and brought together a group of dermatologists, plastic surgeons, and injectors from other specialities to review and discuss current safety-related issues associated with aesthetic injectables. By the end of this meeting, the STF has agreed upon a list of priorities (i.e., areas of concern in the aesthetic industry) and methods of addressing them. The STF concluded that the development of a global AE registry necessitates the development of evidence-based AE protocols. Therefore, in Chapter 6 we report the results of a prospective study wherein the Global Registry of Adverse Clinical Events (GRACE) was developed and validated. Finally, in chapter 7 future aims and plans for improving patient safety in the field of aesthetics are outlined
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".