Treatment of Post-Inflammatory Hyperpigmentation in Skin of Colour: A Systematic Review
Bibliographic record
Abstract
Post inflammatory hyperpigmentation (PIH) affects all skin types with a heightened predilection for darker skin tones. Its course is chronic once developed and treatment is often difficult. This systematic review aims to summarize the treatment outcomes for PIH with a focus on skin of colour (SOC) individuals. A literature search was conducted using MEDLINE (from 1946), Embase (from 1974), PubMed, and Cochrane in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. Results from 48 studies summarized 1356 SOC individuals. The mean age was 29 years (n = 1036) and 78% were female (n = 786). The ethnic prevalence was 70% Black, 27% Asian, and 3% Latin. Overall, 20% were Fitzpatrick skin type (FST) III, 40% FST IV, 34% FST V, and 6% FST VI. Most cases were precipitated by inflammatory conditions (89%) and localized to the face (83%). The most frequently reported interventions were topical retinoids (22%) and laser therapy (17%). Partial improvement was seen in 85% and 66% of participants, respectively. Laser was the only intervention that offered complete resolution in a subgroup of patients (26%); however, there were reported cases of PIH exacerbation following treatment. Chemical peels (9%) and hydroquinone (7%) were among other treatments with less effective outcomes. PIH and its persistence is a prevalent issue, significantly affecting many affected individuals with darker skin tones. Our results show a lack of robust efficacy across all treatment modalities. There is considerable room for improvement in interventions 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 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".