Clinical Trials Perception in Rheumatology Patients: Experience from a Single Rheumatology Tertiary Center
Bibliographic record
Abstract
OBJECTIVE: To investigate the perception and willingness of rheumatology patients to participate in clinical trials. No previous similar studies are available. METHODS: We conducted a cross-sectional survey of rheumatology patients using a questionnaire, which comprised 2 demographic questions, two 5-point Likert opinion questions, 19 true/false/unsure knowledge questions, and 1 open question addressing what would help the participant to gain a better understanding about clinical trials. RESULTS: Eighty-five patients returned the questionnaires (response rate 84.1%). The mean number of correct answers to the 19 knowledge questions was 10.5 ± 2.87. Patients with higher versus lower levels of education had significantly higher knowledge scores (mean correct answers 59.4 ± 13.1 vs 39.8 ± 20.4, p = 0.013). They also expressed greater willingness to take part in research (87.5% vs 48.2%, p < 0.001). The patients who agreed to participate in research provided significantly more correct answers (59.4 ± 15.3% vs 47.7 ± 27.2%, p = 0.032). Poor disease control as the main reason to join a clinical trial correlated well with patients' previous participation in research (r = 0.71; p < 0.05) and the lack of understanding of research principles (defined as less than 50% correct answers to the knowledge questions) correlated with the lack of willingness to participate in clinical trials (r = 0.72; p < 0.05). CONCLUSION: The results of our study revealed that patients lack information about clinical trials (the correct response rate was only slightly above 50%), and that they had a moderate willingness to take part in clinical trials. The need for educational programs about clinical research was highlighted by the participants to the survey.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".