Defining the Minimal Important Change and Meaningful Change Value of the Disease Activity Index for Psoriatic Arthritis: A Chinese Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To determine the minimal important change (MIC) and meaningful change value (MCV) of the Disease Activity Index for Psoriatic Arthritis (DAPSA) and the effect size (ES) of DAPSA. METHODS: This was a retrospective cohort study, recruiting 106 patients who agreed to participate in the research from the Department of Dermatology, Xiangya Hospital, between November 1, 2019, and April 1, 2023. An anchor-based method using linear regression analyses was used to determine the MICs and MCVs of the DAPSA. The anchor question assessed whether the patient's well-being had changed since their previous visit, employing a 5-point Likert scale that ranged from "much improved" to "much deteriorated." RESULTS: The overall MIC value was 8.4 (95% CI 0.01-16.75). The MIC improvement was 9.5 (95% CI 0.89-18.14) and MIC deterioration was 1.1 (95% CI -9.81 to 12.05). The overall MCV was 10.5 (95% CI 4.34-16.72). MCV improvement was 11.4 (95% CI 5.95-16.95) and MCV deterioration was 1.1 (95% CI -9.81 to 12.05). The ES was 0.6. CONCLUSION: A change in DAPSA of 8.4 is indicative of an MIC, offering physicians an additional means to contextualize the patient's perception of disease activity during treatment, and a change in DAPSA of 10.5 is likely to be regarded as MCV. These values can enhance the utility of DAPSA in psoriatic arthritis clinical trials.
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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 | 0.007 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".