Light‐ and laser‐based treatments for granuloma annulare: A systematic review
Bibliographic record
Abstract
BACKGROUND: Granuloma annulare (GA) is challenging to treat, especially when generalized. A systematic review to support the use of light- and laser-based treatments for GA is lacking. METHODS: We performed a systematic review by searching Cochrane, MEDLINE, and Embase. Title, abstract, full-text screening, and data extraction were done in duplicate. Quality appraisal was performed using the Joanna Briggs Institute critical appraisal tool for case series. RESULTS: Thirty-one case series met the inclusion criteria, representing a total of 336 patients. Overall, psoralen ultraviolet light A (PUVA) showed the greatest frequency of cases with complete response (59%, n = 77/131), followed by photodynamic therapy (PDT) (52%, n = 13/25), ultraviolet light B (UVB)/narrowband UVB (nbUVB)/excimer laser (40%, n = 19/47), UVA1 (31%, n = 27/86), and lasers (29%, n = 8/28). Overall across treatment modalities, higher response rates were seen in localized GA compared to generalized GA. CONCLUSIONS: The body of evidence for light- and laser-based treatment of GA is sparse. Our results suggest that PUVA has a high clearance rate for GA but its use may be limited by concerns of carcinogenesis. Although PDT has the second highest clearance rate, adverse effects, small sample sizes, impractical treatment delivery (especially with generalized disease), and long-term concerns of carcinogenesis may limit its use. Although UVB/nbUVB/excimer laser appeared slightly less effective than other light therapies, we recommend UVB/nbUVB/excimer laser therapy as a first-line treatment for patients with generalized GA given wider availability and a favorable long-term safety profile.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".