Post‐inflammatory hyperpigmentation: A systematic review of treatment outcomes
Bibliographic record
Abstract
Post-inflammatory hyperpigmentation is a common consequence of inflammatory dermatoses. It is more common in patients with darker skin and has significant morbidity. This systematic review summarizes treatment outcomes for post-inflammatory hyperpigmentation to help physicians better predict clinical response and improve patient outcomes. Embase, MEDLINE, PubMed databases and clinicaltrials.gov were searched in accordance with PRISMA guidelines using a combination of relevant search terms. Title, abstract and full text screening were done in duplicate. Studies were included if they met our predetermined PICOS framework criteria. Results are presented in descriptive form. In total, 41 studies representing 877 patients were included. Complete response was achieved by laser and energy-based devices in 18.1% (n = 56/309) of patients, topicals in 5.4% (n = 20/369) and combination therapies in 2.4% (n = 4/166). Partial response was achieved by combination modalities in 84.9% (n = 141/166) of patients, topicals in 72.4% (n = 267/369), laser and energy-based devices in 61.2% (n = 189/309) and peels in 33.3% (n = 5/15). Poor to no response occurred with peels in 66.7% (n = 10/15) of patients, topicals in 22.2% (n = 82/369), laser and energy-based devices in 18.1% (n = 56/309) and combination modalities in 12.7% (n = 21/166). Additionally, in 2.6% (n = 8/309) of patients treated with laser and energy-based devices, post-inflammatory hyperpigmentation worsened. Adverse events were reported in 10 patients, all while using topical treatments. In conclusion, the current treatment approaches yielded unsatisfactory rates of complete response. However, combination therapies, laser and energy-based devices and topical therapies showed high rates of partial response. Of note, the majority of post-inflammatory hyperpigmentation cases included were associated with acne, and therefore, the findings and conclusions drawn may have limited applicability to other types of post-inflammatory hyperpigmentation.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".