Ten-Year and Beyond Follow-up After Treatment With Highly Purified Liquid-Injectable Silicone for HIV-Associated Facial Lipoatrophy: A Report of 164 Patients
Bibliographic record
Abstract
BACKGROUND: Highly purified liquid-injectable silicone (LIS) has been established as a permanent agent for off-label correction of HIV-associated facial lipoatrophy (HIV-FLA). However, controversy exists about long-term safety. OBJECTIVE: To establish the safety and efficacy at 10 years or greater of LIS for HIV-FLA. METHODS: Patients from 3 practices with 10-year or greater in-person office follow-up were analyzed to determine the number of LIS treatments and total volume required to achieve optimal correction. The nature of any treated adverse events was noted. RESULTS: One hundred sixty-four patients had 10-year or greater in-office follow-up. All subjects maintained long-term correction with an average of 9 treatments, average of 1.56 mL per treatment, and an average total of 14.1 mL. Two patients had severe adverse events manifesting as temporary facial edema. Four patients experienced mild-to-moderate excess fibroplasia presenting as perceived overcorrection, and 6 patients had nondisfiguring subcutaneous firmness. All adverse events were successfully treatable, mostly with intralesional 5-fluorouracil and triamcinolone. CONCLUSION: Liquid-injectable silicone is an effective long-term treatment option for HIV-FLA. When injected in small quantities with the microdroplet serial puncture technique at monthly or greater intervals, optimal correction appears durable for more than 10 years. Adverse events consisted mostly of excess fibroplasia and were treatable.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".