Identification of Cutpoints for Acceptable Health Status and Important Improvement in Patient-Reported Outcomes, in Rheumatoid Arthritis, Psoriatic Arthritis, and Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: To identify cutpoints reflecting Patient Acceptable Symptom State (PASS) and Minimal Clinically Important Improvement (MCII) in patient-reported multi-attribute health status classification systems and health status measurements among patients with rheumatoid arthritis (RA), ankylosing spondylitis (AS), and psoriatic arthritis (PsA). METHODS: We identified patients with RA, AS, and PsA from the Norwegian disease-modifying antirheumatic drug (DMARD) register (NOR-DMARD). The patients (n = 4225) had started with DMARD and responded to the PASS and MCII anchoring questions at the 3-month followup examination. Receiver operating characteristics (ROC) curves with 80% specificity and the 75th percentile approach were used to identify PASS and MCII cutpoints in the EuroQol-5 Dimensions (EQ-5D) and the Short-Form-6 Dimensions (SF-6D) indexes, but also in other patient-reported outcomes (joint pain and patient global visual analog scale and Modified Health Assessment Questionnaire). RESULTS: The PASS cutpoints estimated with 80% specificity were around 0.70 in EQ-5D in all diseases and around 0.65 in SF-6D. The cutpoints were around 0.65 and 0.60, respectively, when the 75th percentile approach was used. The MCII cutpoints assessed by 80% specificity varied from 0.10 to 0.19 in EQ-5D and from 0.07 to 0.10 in SF-6D. CONCLUSION: The cutpoints for PASS in EQ-5D and SF-6D indicate that PASS corresponds to a health-related quality of life that is far from perfect health. Somewhat different cutpoints were identified for both PASS and MCII with 80% specificity versus the 75th percentile method.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".