Information is a key prerequisite for perceived relevance of patient reported outcome data (PRO data): A multicenter questionnaire study
Bibliographic record
Abstract
Objective: The aim of this study was to investigate the relevance of patient reported outcome data (PRO data) as assessed by arthritis patients in a Danish hospital setting.Methods: The study was conducted as a multicenter questionnaire survey comprising patients with rheumatoid arthritis, ankylosing spondylitis, and psoriatic arthritis at three rheumatology outpatient clinics. Respondents with experience of reporting PRO data were recruited. The recruitment took place in March 2017.Results: A total of 98 respondents were included. We found significant correlation between respondents’ level of information about PRO data and the perceived relevance of PRO data questions. Remarkably, a third of the respondents stated that PRO data prepared neither themselves nor the healthcare professionals for the consultation, while 40% found that their PRO data responses were not used during consultations with healthcare professionals.Conclusions: The respondents’ assessment of the relevance of PRO data depended on the information offered to them. In recognition of its potential as a tool for patient involvement, the use of PRO data should be formally integrated in routine clinical care.
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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 | 0.024 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".