Trends in the management of hidradenitis suppurativa in the Middle East region: a systematic review
Bibliographic record
Abstract
BACKGROUND: Hidradenitis suppurativa (HS) is a chronic, painful, and severely debilitating inflammatory disease that affects apocrine gland-bearing skin. There is no universal consensus on the management of hidradenitis suppurativa (HS). Current available guidelines are from western countries, which may be of limited use in other parts of the world. In this study, we aim to identify trends in the management of HS in the Middle East (ME) and its differences with the West. METHODS: A systematic literature review was performed to identify all studies in HS management performed by experts in the ME from database inception to June 2020 using PubMed, EMBASE, and Cochrane Library databases. Only original articles published in English language were included. Guidelines from western countries were used for comparison. RESULTS: Fifty-four eligible studies comprising 7649 HS patients from the ME region and nine guidelines from the West were included. No established guidelines were found for the management of HS in the ME. Sex ratio of HS patients in the ME is different in that men reported to be affected at a higher rate than in the West. Different antibiotic regimens were recommended in the ME, and the experts in the ME tend to be more procedure intensive than experts in the West. CONCLUSIONS: Establishing a guideline for HS management in the ME is recommended to address unique considerations in the countries in this region.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".