Which Attributes Are Most and Least Important to Patients When Considering Gout Flare Burden Over Time? A Best-worst Scaling Choice Study
Bibliographic record
Abstract
OBJECTIVE: Several factors contribute to the patient experience of gout flares, including pain intensity, duration, frequency, and disability. It is unknown which of these factors are most important to patients when considering flare burden over time, including those related to the cumulative experience of all flares, or the experience of a single worst flare. This study aimed to determine which flare attributes are the most and least important to the patient experience of flare burden over time. METHODS: Participants with gout completed an anonymous online survey. Questions were aimed at identifying which attributes of gout flares, representing both individual and cumulative flare burden, were the most and least important over a hypothetical 6-month period. A best-worst scaling method was used to determine the importance hierarchy of the included attributes. RESULTS: Fifty participants were included. Difficulty doing usual activities during the worst flare and pain of the worst flare were ranked as the most important, whereas average pain of all flares was considered the least important. Overall, attributes related to the single worst gout flare were considered more important than attributes related to the cumulative impact of all flares. CONCLUSION: When thinking about the burden of gout flares over time, patients rank activity limitation and pain experienced during their worst gout flare as the most important contributing factors, whereas factors related to the cumulative impact of all flares over time are relatively less important.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".