A systematic review of filler agents for aesthetic treatment of HIV facial lipoatrophy (FLA)
Bibliographic record
Abstract
HIV facial lipoatrophy (FLA) is characterized by facial volume loss. HIV FLA affects the facial contours of the cheeks, temples, and orbits, and is associated with social stigma. Although new highly active antiretroviral therapy medications are associated with less severe FLA, the prevalence of HIV FLA among treated individuals exceeds 50%. The goal of our systematic review is to examine published clinical studies involving the use of filler agents for aesthetic treatment of HIV FLA and to provide evidence-based recommendations based on published efficacy and safety data. A systematic review of the published literature was performed on July 1, 2015, on filler agents for aesthetic treatment of HIV FLA. Based on published studies, poly-L-lactic acid is the only filler agent with grade of recommendation: B. Other reviewed filler agents received grade of recommendation: C or D. Poly-L-lactic acid may be best for treatment over temples and cheeks, whereas calcium hydroxylapatite, with a Food and Drug Administration indication of subdermal implantation, may be best used deeply over bone for focal enhancement. Additional long-term randomized controlled trials are necessary to elucidate the advantages and disadvantages of fillers that have different biophysical properties, in conjunction with cost-effectiveness analysis, for treatment of HIV FLA.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".