Skin diseases in displaced populations: a review of contributing factors, challenges, and approaches to care
Bibliographic record
Abstract
There are 70.8 million persons displaced worldwide due to war, persecution, and violence. Eighty percent of displaced persons reside in low- and middle-income countries with limited healthcare resources. Cutaneous diseases are commonly reported among displaced persons owing to numerous interrelated factors such as inadequate housing, overcrowding, food insecurity, environmental exposures, violence including torture, and breakdown of healthcare infrastructure. Diagnosis and management of these conditions, as well as an understanding of the context in which they present, is crucial to providing dermatologic care for displaced populations worldwide. Herein, we define displaced populations and, within this context, review the epidemiology of skin diseases, discuss pertinent skin conditions, examine challenges to care provision, and present approaches for improving dermatologic care. Inflammatory and communicable infectious disorders are the most common skin diseases seen in displaced populations. Other relevant conditions include skin manifestations of heat injuries, cold injuries, immersion foot syndromes, macronutrient and micronutrient deficiencies, torture, and sexual and gender-based violence. Provision of dermatologic care to displaced populations is hampered by limited diagnostic and therapeutic resources and specialist expertise. Medical screening for cutaneous disorders, context-relevant dermatology training, and telemedicine are potential tools to improve diagnosis and management of skin diseases in displaced 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".