Development and First Use of the Patient’s Qualitative Assessment of Treatment (PQAT) Questionnaire in Type 2 Diabetes Mellitus to Explore Individualised Benefit–Harm of Drugs Received During Clinical Studies
Bibliographic record
Abstract
INTRODUCTION: Individualised benefit-harm assessments can help identify patient-perceived benefits and harms of a treatment, and associated trade-offs that may influence patients' willingness to use a treatment. This research presents the first use of a patient-reported outcome measure designed to assess patient-perceived benefits and disadvantages of drugs received during clinical studies. METHODS: The Patient's Qualitative Assessment of Treatment (PQAT) was developed in English and cognitively tested with US (n = 4) and Canadian (n = 3) patients with type 1 and type 2 diabetes mellitus (T2DM). The revised version of the PQAT comprises three qualitative open-ended questions focused on the benefits and disadvantages of treatment and reasons why patients would choose to continue/discontinue treatment. A final quantitative question asks patients to evaluate the balance between benefits and disadvantages using a 7-point scale. The revised version of the questionnaire was administered as an exploratory endpoint in a phase II clinical trial for a new injectable treatment for T2DM. Qualitative data were analysed using thematic analysis, and relationships between qualitative and quantitative data were identified. RESULTS: Patient-reported benefits of treatment administered during the clinical trial included clinical markers of efficacy and subjective markers. Disadvantages reported by patients were mainly related to drug adverse effects or to the mode of administration. Of the 57 patients completing the PQAT, 70.2% reported being willing to continue treatment, with 59.6% reporting that the benefits outweighed the disadvantages. The reported benefits of feeling better and improved energy levels were more likely to be associated with a more positive ratio (70% and 71.4%, respectively), while the disadvantages of fatigue, headaches, and stomach pain were associated with a negative ratio and patients not being willing to continue the treatment. CONCLUSIONS: The PQAT is a unique patient-reported outcome tool designed to aid understanding patients' real experience of benefits and disadvantages of a treatment. It combines the richness of qualitative data with quantitative data-information valuable for various stakeholders to make well-informed treatment decisions. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02973321.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".