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Record W3087550983 · doi:10.1177/1203475420957633

Prophylaxis of Post-Inflammatory Hyperpigmentation From Energy-Based Device Treatments: A Review

2020· review· en· W3087550983 on OpenAlexaff
Ian T.Y. Wong, Vincent Richer

Bibliographic record

VenueJournal of Cutaneous Medicine and Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicDermatologic Treatments and Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDermatologyHyperpigmentationTopical agentsIntensive care medicine

Abstract

fetched live from OpenAlex

Post-inflammatory hyperpigmentation (PIH) is an acquired hypermelanosis that can result from inflammatory dermatologic disease, trauma, or iatrogenesis from procedures. This condition disproportionately affects individuals with skin of color, and it can place a significant psychosocial burden on affected patients. The management of PIH is, therefore, of great interest to clinicians, especially dermatologists. The treatment of established PIH has long been a principal focus within the literature, with publications on the topic outnumbering publications on prophylaxis of PIH. Prophylaxis strategies to prevent PIH vary greatly in clinical practice, likely due to the absence of an evidence-based consensus. Published approaches to PIH prophylaxis include pretreatment (topical alpha hydroxy acids, retinoids, hydroquinone, and brimonidine) and post-treatment strategies (photoprotection, corticosteroids, and tranexamic acid). This review will examine the current literature on prophylaxis of PIH from energy-based device treatments.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.067
GPT teacher head0.357
Teacher spread0.290 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations15
Published2020
Admission routes1
Has abstractyes

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