Patient prioritisation of items to develop the Patient‐Reported Impact of Dermatological Diseases measure: A global Delphi study
Bibliographic record
Abstract
BACKGROUND: The Global Research on the Impact of Dermatological Diseases (GRIDD) project is developing the new Patient-Reported Impact of Dermatological Diseases (PRIDD) measure. PRIDD measures the impact of dermatological conditions on the patient's life. OBJECTIVES: This study aimed to seek consensus from patients on which items to prioritize for inclusion in PRIDD. METHODS: A modified, two-round Delphi study was conducted. Adults (≥18 years) with dermatological conditions were recruited. The survey consisted of a demographic's questionnaire and 263 potential impact items in six languages. Quantitative data used Likert-type ranking scales and analysed against consensus criteria. Qualitative data collected free text responses for additional feedback and a framework analysis was conducted. RESULTS: 1154 people representing 90 dermatological conditions from 66 countries participated. Items were either removed (n = 79), edited (n = 179) or added (n = 2), based on consensus thresholds and qualitative feedback. Results generated the first draft of PRIDD with 27 items across five impact domains. CONCLUSION: This Delphi study resulted in the draft version of PRIDD, ready for psychometric testing. The triangulated data helped refine the existing conceptual framework of impact. PRIDD has since been pilot tested with patients and is currently undergoing psychometric testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".