Prevention of Post‐Inflammatory Hyperpigmentation in Skin of Colour: A Systematic Review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Post-inflammatory hyperpigmentation (PIH) impacts all skin tones with a heightened predilection for Fitzpatrick skin types (FST) III-VI. Preventative measures include pre- and post-intervention approaches, such as sunscreen and corticosteroids. This systematic review aims to summarise the preventative measure outcomes for skin of colour individuals. METHODS: A literature search was conducted using MEDLINE (from 1946) and Embase (from 1974) in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. RESULTS: Of 14 studies, 369 cases were included. The mean age was 38 years (n = 293) and 72% were female (n = 265). All patients were of Asian ethnicity, and 42% were of FST III, 54% FST IV, and 4% FST V. Nearly all cases were precipitated by laser therapy (> 95%), and the face was the most reported location (85%). The most successful preventative measure was sunscreen alone or combined with other ingredients. Less successful outcomes were seen with topical corticosteroids and systemic tranexamic acid, while cooling air devices exacerbated the development of PIH. CONCLUSION: Overall, only sunscreen consistently prevented the incidence of PIH; however, the severity of the ensuing PIH may be diminished with other measures. There is considerable room for improved preventative strategies for at-risk populations.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".