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Record W4392542640 · doi:10.1097/dss.0000000000004151

A Systematic Review on Treatment Outcomes of Striae

2024· review· en· W4392542640 on OpenAlexaff
Catherine Zhu, Lorena Alexandra Mija, Kaouthar Koulmi, Benjamin Barankin, Ilya Mukovozov

Bibliographic record

VenueDermatologic Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsMedicineMEDLINEDermatologySurgery

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.168
GPT teacher head0.438
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2024
Admission routes1
Has abstractyes

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