<u>G</u> lobal <u>R</u> egistry of <u>A</u> dverse <u>C</u> linical <u>E</u> vents (GRACE <sup>©</sup> ): A Prospective, Multicenter, Observational Cohort Evaluating Complications Associated With Aesthetic Injectables
Bibliographic record
Abstract
Background: A review of Health Canada’s post-market surveillance database has revealed that the reporting of adverse events (AEs) following aesthetic injectable treatments is significantly underreported. To increase reporting, investigators have recently developed a novel Electronic Data Capture system: The Global Registry of Adverse Clinical Events (GRACE © ). Objective: To identify the incidence of AEs associated with aesthetic injectable treatments. Methods: Aesthetic clinicians from 10 Canadian sites were recruited. Demographic and clinical data were recorded within the database, which included over 45 patient variables. Results: Throughout the active phase of the trial (duration: 27 months), 123,124 injectable treatments were conducted. One hundred and eleven patients, experiencing a total of 235 AEs, were entered into the portal. This equated to an AE incidence rate of 0.19%, per treatment. Thirty unique products were associated with AEs. In total, there were 112/235 (47.66%) mild, 88/235 (37.45%) moderate, and 35/235 (14.90%) severe AEs. The most common complication (n = 48/235; 20.43%) was swelling, with a prevalence of 0.04%. Of the documented AEs, only 5 were reported to other sources, including 1 case being reported to Health Canada and 4 cases to the respective product manufacturer. Conclusions: The initial feasibility of a registry assessing safety outcomes following injectable treatment has been demonstrated. Findings support that the implementation of the GRACE Portal is an effective outreach strategy for increasing AE reporting by health care professionals. The data represent a more accurate depiction of the safety profile of approved aesthetic injectables in Canada.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".