Alternative Cosmetic and Medical Applications of Injectable Deoxycholic Acid: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Beyond submental fat reduction, injectable deoxycholic acid (DCA) has gained popularity in recent years for various minimally invasive lipolysis applications. OBJECTIVE: To summarize and evaluate the evidence of off-label uses of injectable DCA. METHODS: MEDLINE, Embase, CINAHL, Web of Science, and CENTRAL were searched. The outcomes measured included applications of DCA, treatment regimen, and its efficacy. An overall success rate for each condition was calculated based on the improvement defined in the included studies. RESULTS: Eleven studies evaluated the cosmetic use of DCA for excess adipose tissue on various anatomical locations. The outcomes were evaluated at time points ranging from 1 to 21 months post-treatment, with overall success rates over 85%. Eight case reports and series reported the success of using DCA treating lipomas, xanthelasmas, paradoxical adipose hyperplasia, fibrofatty residue of infantile hemangioma, piezogenic pedal papules, and HIV-associated lipohypertrophy. Although the preliminary efficacies were high, the overall recommendations for off-label uses are weak because of the lack of high-level studies. CONCLUSION: The review emphasizes the diversity of injectable DCA as a minimally invasive technique for lipolysis. Further high-level studies demonstrating consistent treatment regimens and methods of evaluation are warranted to make more definitive recommendations regarding off-label DCA use.
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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) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".