Treatment of Lentigines: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Treatments for solar lentigines include topical and physical therapies, including chemical peels, lasers, intense pulsed light, and cryotherapy. A direct comparison of treatment methods and their efficacy is lacking. OBJECTIVE: To compare treatment efficacy and adverse events for different treatment modalities for lentigines. METHODS: Cochrane, MEDLINE, and Embase databases were searched on August 25, 2021. Studies were included if they met our predetermined population, intervention, comparator, outcomes, study design framework. Results are presented in narrative form. RESULTS: Forty-eight articles met the inclusion criteria, representing a total of 1,763 patients. Overall, combination-based treatments showed the greatest frequency of cases with complete response (65%, n = 299/458), followed by laser-based treatments (43%, n = 395/910), topical retinoids (21%, n = 12/57), cryotherapy (15%, n = 25/169), and peels (6%, n = 8/125). Adverse events occurred most commonly while using topical retinoids (82%, n = 23/28), followed by combination-based treatments (39%, n = 184/466), cryotherapy (33%, n = 47/144), laser-based treatments (23%, n = 173/738), and peels (19%, n = 21/110). CONCLUSION: Despite heterogeneity of included study designs, patient populations, treatment regimens, and outcome measures, our results suggest that combination-based treatments and laser-based treatments were the most efficacious treatment modalities. Although cryotherapy was previously considered first-line, our results show that it has substantially lower pooled response rates compared with other treatment modalities.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".