<p>Assessing Preferences for Rare Disease Treatment: Qualitative Development of the Paroxysmal Nocturnal Hemoglobinuria Patient Preference Questionnaire (PNH-PPQ©)</p>
Bibliographic record
Abstract
PURPOSE: To develop a patient preference questionnaire (PPQ) assessing eculizumab and ravulizumab treatment for paroxysmal nocturnal hemoglobinuria (PNH). PATIENTS AND METHODS: was consistent with Food and Drug Administration guidelines for patient-reported outcome measure development, and included 1) a targeted literature review; 2) PNH expert clinician input on treatment preferences; 3) review of existing qualitative data on the PNH treatment and disease experience; 4) concept elicitation interviews with 8 PNH patients who received eculizumab and/or ravulizumab; 5) translatability review; and 6) cognitive debriefing with 5 patients. Interview participants were recruited through a United Kingdom PNH patient advocacy group and a Canadian clinical site involved in clinical trial ALXN1210-PNH-302. RESULTS: Six themes were identified as most relevant to the PNH treatment experience from the concept elicitation interviews: disease symptoms (n=8/8); treatment frequency (n=7/8); quality of life impact of treatment/disease (n=7/8); treatment burden (n=7/8); treatment efficacy (n=5/8); and treatment side effects (n=5/8). An initial list of 88 preference questions was reduced to 11 highly relevant and non-redundant questions reflecting the 6 themes. Cognitive interview participants unanimously agreed that the PNH-PPQ instructions were clear; response options were understandable, easy to use, and provided enough choices; and the questions captured the factors that inform treatment preferences. DISCUSSION: When new drugs have similar efficacy to existing medications, documenting patient preferences is important for confirming patient benefit from the new medication. Understanding what matters most to patients is essential for delivering patient-centered care and may play a particularly significant role in treatment decision making. The availability of such a tool may be especially important as new orphan drugs are developed and patients with rare diseases have more than one treatment option to consider. CONCLUSION: The PNH-PPQ provides a patient-centered approach for evaluating preferences for the treatment of PNH. The PNH-PPQ has subsequently assessed patient preference in the clinical trial sub-study ALXN1210-PNH-302s.
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".