Patient-reported well-being in value-based care using tildrakizumab in a real-world setting: protocol of a multinational, phase IV, 1-cohort prospective observational study (the POSITIVE study)
Bibliographic record
Abstract
INTRODUCTION: Psoriasis is a chronic inflammatory skin disease that negatively impacts the quality of life of patients and their families. However, the most commonly used decision-making tools in psoriasis, Psoriasis Area and Severity Index (PASI), Physician Global Assessment (PGA) and Dermatology Life Quality Index (DLQI), do not fully capture the impact of psoriasis on patients' lives. In contrast, the well-established 5-item WHO Well-being Index (WHO-5) assesses the subjective psychological well-being of patients. Moreover, while drug innovations became available for psoriasis, data on the impact of these therapies on patients' lives and their closest environment (family, physicians) are limited. This study will assess the effect of tildrakizumab, an interleukin-23p19 inhibitor, on the overall well-being of patients with moderate-to-severe psoriasis. Moreover, the long-term benefit of tildrakizumab on physicians' satisfaction and partners' lives of patients with psoriasis will be evaluated. METHODS AND ANALYSIS: This non-interventional, prospective, observational, real-world evidence study will involve multiple sites in Europe and approximately 500 adults with moderate-to-severe psoriasis treated with tildrakizumab. Each patient will be followed for 24 months. The primary endpoint is well-being measured by the WHO-5 questionnaire. Key secondary endpoints include Physician's Satisfaction and partner's quality of life (FamilyPso). Other endpoints will evaluate skin-generic quality of life (DLQI-R), Treatment Satisfaction Questionnaire for Medication (TSQM-9), Treatment-related Patient Benefit Index 'Standard', 10 items (PBI-S-10) and work productivity and activity impairment due to psoriasis (WPAI:PSO). Statistical analyses will be based on observed cases. Multiple imputations will be performed as a sensitivity analysis, and adverse events will be reported. ETHICS AND DISSEMINATION: The study will be conducted according to the protocol, which received ethics committee approval and applicable regulatory requirements of each participating country. The results will be disseminated through scientific publications and congress presentations. TRAIL REGISTRATION NUMBER: ClinicalTrials.gov Identifier: NCT04823247 (Pre-results).
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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 | 0.023 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".