Application of Intense Pulsed Light in the Treatment of Dermatologic Disease: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: The Food and Drug Administration (FDA) has approved intense pulsed light (IPL) devices for the treatment of a variety of benign pigmentary and vascular lesions, but the range of disease amenable to IPL treatment continues to expand, and there are no evidence-based clinical guidelines for its use in FDA-approved and off-label indications. OBJECTIVE: To provide evidence-based recommendations to guide physicians in the application of IPL for the treatment of dermatologic disease. EVIDENCE REVIEW: A literature search of the CENTRAL (1991 to May 6, 2013), EMBASE (1974 to May 6, 2013), and MEDLINE in-process and nonindexed citations and MEDLINE (1964 to present) databases was conducted. Studies that examined the role of IPL in primary dermatologic disease were identified, and multiple independent investigators extracted and synthesized data. Recommendations were based on the highest level of evidence available. FINDINGS: Level 1 evidence was found for the use of IPL for the treatment of melasma, acne vulgaris, and telangiectasia. Level 2 evidence was found for the treatment of lentiginous disease, rosacea, capillary malformations, actinic keratoses, and sebaceous gland hyperplasia. Level 3 or lower evidence was found for the treatment of poikiloderma of Civatte, venous malformations, infantile hemangioma, hypertrophic scars, superficial basal cell carcinoma, and Bowen's disease. CONCLUSIONS: IPL is an effective treatment modality for a growing range of dermatologic disease and in some cases may represent a treatment of choice. It is typically well tolerated. Further high-quality studies are required.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".