European S2k Guideline on Chronic Pruritus
Bibliographic record
Abstract
Pruritus is a frequent symptom in medicine. Population-based studies show that every 5th person in the general population has suffered from chronic pruritus at least once in the lifetime with a 12-month incidence of 7%. In patient populations its frequency is much higher depending on the underlying cause, ranging from around 25% in haemodialysis patients to 100% in skin diseases such as urticaria and atopic dermatitis (AD). Pruritus may be the result of a dermatological or non-dermatological disease. Especially in non-diseased skin it may be caused by systemic, neurological or psychiatric diseases, as well as being a side effect of medications. In a number of cases chronic pruritus may be of multifactorial origin. Pruritus needs a precise diagnostic work-up. Management of chronic pruritus comprises treatment of the underlying disease and topical treatment modalities, including symptomatic antipruritic treatment, ultraviolet phototherapy and systemic treatment. Treating chronic pruritus needs to be targeted, multimodal and performed in a step-wise procedure requiring an interdisciplinary approach. We present the updated and consensus based (S2k) European guideline on chronic pruritus by a team of European pruritus experts from different disciplines. This version is an updated version of the guideline that was published in 2012 and updated in 2014 (www.euroderm.org).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".