A Systematic Review on Treatment Outcomes of Striae
Bibliographic record
Abstract
BACKGROUND: Striae are fine lines on the body that occur following rapid skin stretching (i.e., following pregnancy, puberty, weight change). The aim of this systematic review was to assess the current literature on treatment outcomes associated with striae. OBJECTIVE: (1) To assess the efficacy and safety of different treatment options reported for striae and (2) to determine the most efficient treatment options for each subtype of striae. METHODS: A systematic search was performed on MEDLINE, Embase, and PubMed with no publication date or language restrictions. All articles with original data and treatment outcomes were included. RESULTS: One hundred fifty-one studies on the treatment of striae met inclusion criteria (83% female, mean age at diagnosis = 30.2), and 4,806 treatment outcomes of striae were described. Energy-based devices were the most reported modality (56%; n = 2,699/4,806), followed by topicals (19%; n = 919/4,806) and combinations (12%; n = 567/4,806). The highest rates of complete response were injection-based devices for striae distensae (7%; n = 12/172), CO 2 lasers for striae alba (4%; n = 12/341), and platelet-rich plasma injections for striae rubra (31%; n = 4/13). CONCLUSION: Treatment options for striae are varied, likely indicating a lack of effective treatments due to the diversity in striae subtypes. Improved outcomes in striae management may be achieved with additional research on factors that predict treatment response.
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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 | 0.009 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".