Bringing It All Together: A Novel Approach to the Development of Response Criteria for Chronic Gout Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: To review a novel approach for constructing composite response criteria for use in chronic gout clinical trials that implements a method of multicriteria decision-making. METHODS: Preliminary work with paper patient profiles led to a restricted set of core-set domains that were examined using 1000Minds™ by rheumatologists with an interest in gout, and (separately) by OMERACT registrants prior to OMERACT 10. These results and the 1000Minds approach were discussed during OMERACT 10 to help guide next steps in developing composite response criteria. RESULTS: There were differences in how individual indicators of response were weighted between gout experts and OMERACT registrants. Gout experts placed more weight upon changes in uric acid levels, whereas OMERACT registrants placed more weight upon reducing flares. Discussion highlighted the need for a "pain" domain to be included, for "worsening" to be an additional level within each indicator, for a group process to determine the decision-making within a 1000Minds exercise, and for the value of patient involvement. CONCLUSION: Although there was not unanimous support for the 1000Minds approach to inform the construction of composite response criteria, there is sufficient interest to justify ongoing development of this methodology and its application to real clinical trial data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".