Commentary on: Facial Dermal Filler Injection and Vaccination: A 12-Year Review of Adverse Event Reporting and Literature Review
Bibliographic record
Abstract
See the Original Article here. Soft tissue filler treatments in facial aesthetics have become popular in recent years due to the increased availability of nonanimal filler made from hyaluronic acid (HA).1 Recent statistics ranked HA filler second in the top 5 nonsurgical procedures performed worldwide, with an increase of 30.3% in the numbers of treatment executed over the previous year.2,3 HA fillers are closely related to the ideal cosmetic filler. The latter should be biodegradable, biocompatible, versatile, predictable, effective, safe, nonmigratory, noncarcinogenic, and immunologically alert.4 In many studies, HA fillers were shown to be superior to bovine collagen and safe, and to have a high patient satisfaction rate, while offering outstanding results for full face rejuvenation.5,6 However, although rare, delayed inflammatory reactions (DIRs) are possible complications of HA fillers. Since the COVID-19 pandemic, many case reports of DIRs secondary to vaccinations have been published, attracted the media's attention, and warranted further investigation.7,8 I commend the authors for addressing the latter in “Facial Dermal Filler Injection and Vaccination: A 12-Year Review of Adverse Event Reporting and Literature Review,” for which they performed a search with the MAUDE (Manufacturer and User Facility Device Experience) database and a PubMed (National Institutes of Health, Bethesda, MD) literature review.9
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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".