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Use of Hyaluronic Acid for Soft Tissue Augmentation of HIV-Associated Facial Lipodystrophy

2006· article· en· W2031795168 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDermatologic Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicFacial Rejuvenation and Surgery Techniques
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsLipodystrophyMedicineLipoatrophyHyaluronic acidAntiretroviral therapyAdverse effectHuman immunodeficiency virus (HIV)Soft tissueDermatologySurgeryViral loadInternal medicineVirology

Abstract

fetched live from OpenAlex

BACKGROUND: Lipodystrophy syndrome is a devastating complication of antiretroviral therapy in individuals with human immunodeficiency virus (HIV). The appearance of the associated facial lipoatrophy can be demoralizing and stigmatizing for the affected individuals to a point at which it may compromise their compliance with antiretroviral medication. OBJECTIVE: We describe the use of hyaluronic acid as an intradermal filler for correction of this disfiguring problem. METHODS: We treated five patients with grade 2 to 3 facial lipoatrophy. Each patient received approximately 5 to 6 cc in total of hyaluronic acid in the malar area via intradermal injection. RESULTS: There were no adverse events. We found that this technique provided a good cosmetic result with high patient satisfaction. At 6-month follow-up, sustained longevity was observed. CONCLUSIONS: We propose the use of hyaluronic acid for HIV-associated facial lipoatrophy as an efficacious and safe, but temporary, option for this problem until a more cost-effective option is available.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.287
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it