European Guideline on Chronic Pruritus
Bibliographic record
Abstract
Elke WEISShAAr1, Jacek C. SzEpIEToWSkI2, Ulf DArSoW3, Laurent MISEry4, Joanna WALLENgrEN5, Thomas METTANg6, Uwe gIELEr7, Torello LoTTI8, Julien LAMbErT9, peter MAISEL10, Markus STrEIT11, Malcolm W. grEAVES12, Andrew CArMIChAEL13, Erwin TSChAChLEr14, Johannes rINg3 and Sonja STaNDEr15 1Department of Clinical Social Medicine, Environmental and Occupational Dermatology, Ruprecht-Karls-University Heidelberg, Germany, 2Department of Dermatology, Venereology and Allergology, Wroclaw Medical University, Poland, 3Department of Dermatology and Allergy Biederstein, Technical University Munchen and ZAUM Center for Allergy and Environment, Munich, Germany, 4Department of Dermatology, University Hospital Brest, France, 5Department of Dermatology, Lund University, Sweden, 6German Clinic for Diagnostics, Nephrology, Wiesbaden, 7Department of Psychosomatic Dermatology, Clinic for Psychosomatic Medicine, University of Giessen, Giessen, Germany, 8Department of Dermatology, University of Florence, Italy, 9Department of Dermatology, University of Antwerpen, Belgium, 10Department of General Medicine, University Hospital Muenster, Germany, 11Department of Dermatology, Kantonsspital Aarau, Switzerland, 12Department of Dermatology, St. Thomas Hospital Lambeth, London, 13Department of Dermatology, James Cook University Hospital Middlesbrough, UK, 14Department of Dermatology, Medical University Vienna, Austria and 15Department of Dermatology, Competence Center for Pruritus, University Hospital Muenster, Germany
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".