Host Tissue Interaction, Fate, and Risks of Degradable and Nondegradable Gel Fillers
Bibliographic record
Abstract
BACKGROUND: A constantly increasing number of gel fillers for aesthetic and reconstructive purposes have been introduced during the last 20 years. Most of the new ones are modified versions of the original collagen and hyaluronic acid gels. They have been reconstructed, often by adding cross-bindings to the polymer in order to obtain a more dense molecular structure, which will prolong degradation and filling effect of the gel. Other gel fillers contain particles of organic (poly-lactic acid) or inorganic (calcium hydroxylapatite) material, which have been used in human tissue for other purposes (degradable suture material and bone cement, respectively). The permanent fillers (silicone oil and polyacrylamide gel) have been used for many years, silicone mainly in the US and polyacrylamide gel in most countries outside the US and Canada. OBJECTIVE: Complications occur, and they appear to be more frequent with particulated fillers, polyacrylamide gel and silicone oil. However, these complications differ in nature and depend on the filler type used. METHODS AND MATERIALS: This overview presents the different gel filler types, how they interact with host tissue, and what can go wrong. The results and conclusion are based on experimental and clinical observations coupled with a search of the literature. RESULTS AND CONCLUSION: Complications following homogenous hydrogels are caused by infection with bacteria, which have been inserted into the gel during injection. If not treated with relevant antibiotics (but instead steroids or large doses of NSAIDs) the bacteria form a biofilm, which gives rise to a low-grade chronic infection that is resistant to antibiotics. Complications following particulated gels and silicone oil are not known, but bacteria in a biofilm and/or endotoxins released by these is a possibility which deserves further investigations, primarily by using the fluorescence in situ hybridization (FISH) technique.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".