Optimal Practices in the Delivery of Aesthetic Medical Care to Patients on Immunosuppressants and Immunomodulators: A Systematic Review of the Literature
Bibliographic record
Abstract
Nonsurgical aesthetic procedures have been steadily growing in popularity among patients of all ages and ethnicities. At present, the literature remains devoid of guidelines on optimal practices in the delivery of aesthetic medical care to patients on immunosuppressant medications. The authors of this review sought to determine the physiologic responses of immunocompromised patients related to outcomes and potential complications following nonsurgical aesthetic procedures, and to suggest recommendations for optimal management of these patients. A comprehensive systematic review of the literature was performed to identify clinical studies of patients who had undergone nonsurgical aesthetic procedures while immunosuppressed. Forty-three articles reporting on 1690 immunosuppressed patients who underwent filler injection were evaluated, of which the majority (99%; 1682/1690) were HIV patients, while the remaining 8 were medically immunosuppressed. The complication rate of filler in this population was 28% (481/1690), with subcutaneous nodules the most frequently reported adverse event. A detailed synthesis of complications and a review of the inflammatory responses and impact of immunosuppressants and HIV infection on filler complications is presented. The authors concluded that patients on immunomodulatory medications may be at increased risk of filler granuloma relative to the general population, while patients on immunosuppressants may be at increased risk of infectious complications. Rudimentary guidelines for optimal preprocedural patient assessment, aseptic technique, injection technique, and antibacterial and antiviral prophylaxis are reviewed. Ongoing advancements in our understanding of the mechanisms underlying these inflammatory processes will undoubtedly optimize management in this patient population.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".