The <scp>SWITCH</scp> algorithm: An expert consensus on treat‐to‐target criteria for chronic prurigo
Bibliographic record
Abstract
BACKGROUND: To date, the management of chronic prurigo (CPG; prurigo nodularis) relies on expert consensus and guidelines without providing detailed recommendations on long-term treatment management. OBJECTIVES: This study was initiated by an expert committee to define, validate and reach consensus on treat-to-target (T2T) criteria and a decision-supporting treatment algorithm for CPG. METHODS: A prospective, single-centre, non-interventional study was conducted in adult CPG patients at a moderate-to-severe disease stage, experiencing intense itch (NRS ≥7; Group A), and in patients who considered themselves successfully treated (Group B). The patients answered questions about their most severe symptoms and essential therapy goals. A committee of 14 field experts evaluated the study results, identified outcome tools from clinical trials and defined and weighted T2T criteria. Based on this, a treatment algorithm was developed through an iterative consensus-building method and anonymous voting. RESULTS: 171 patients (Group A, n = 96; Group B, n = 75) were interviewed. Itch (99.4%), the need to scratch (48%), and pruriginous lesions (45.6%) were the most frequently reported severe symptoms. Pruritus relief (over complete pruritus resolution) and healing of lesions were the key therapeutic goals. In clinical trials, the most frequently used instruments were assessments of worst/peak itch intensity, disease severity via IGA, DLQI and sleep quality. According to the results, the experts defined initial itch control as NRS ≤3, lesion healing with IGA-S 0/1, and burden, along with their respective clinically measurable parameters, as T2T criteria. These criteria, with predefined cut-off values, serve as the decision-tree keys that constitute the treatment algorithm presented here, facilitating a decision to maintain or switch the treatment. CONCLUSIONS: The T2T criteria and the SWITCH treatment algorithm offer a valid and systematic method for evaluating and refining CPG treatment based on patient-reported and expert-validated data. This will help to improve the treatment of patients with CPG.
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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 | 0.142 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".