Complications in the Cosmetic Dermatology Patient
Bibliographic record
Abstract
BACKGROUND: Over recent decades, the options available to patients for cosmetic rejuvenation have expanded dramatically. The range of options commonly available to patients now includes neuromodulators, fillers, sclerotherapy, chemical peels, liposculpture, lasers, and lights and other energy devices and continues to grow. As with all therapeutic interventions, these cosmetic dermatologic procedures are not without risk. Timely recognition of complications and intervention are paramount for optimal patient outcomes. OBJECTIVE: Part 1 of this review will focus on the common complications of injectable cosmetic procedures, such as neuromodulators, fillers, and sclerotherapy. The second part will discuss the complications of chemical peels, lasers, light and energy devices, and fat removal procedures. MATERIALS AND METHODS: A MEDLINE search was performed on cosmetic dermatology complications from 1989 to 2015, and results are summarized. Practical considerations of these complications are also provided. RESULTS: Reports of complications after neuromodulator, injectable hyaluronic acid, calcium hydroxylapatite, poly-L-lactic acid, polymethylmethacrylate, sclerotherapy, fat transfer, liposuction, cryolipolysis, chemical peels, lasers, and light sources, such as Q-switched laser, intense pulsed light, nonablative and ablative resurfacing lasers, were found. CONCLUSION: Review of the literature revealed multiple management options for potential complications of the multitude of cosmetic dermatology procedures now available to patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".