Faculty Opinions recommendation of Application of composite disease activity scores in psoriatic arthritis to the PRESTA data set.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to compare the performances of the Modified Composite Psoriatic Disease Activity Index (mCPDAI) and the Disease Activity index for PSoriatic Arthritis (DAPSA) in an interventional study of etanercept in psoriatic arthritis.METHODS: The components of the CPDAI and DAPSA were extracted using PRESTA (Psoriasis Randomized Etanercept STudy in subjects with psoriatic Arthritis) study data. Data for four of the five domains of the CPDAI-thus an mCPDAI-were available: joints, skin, dactylitis and enthesitis (spinal involvement was not assessed). Domains in the calculation of DAPSA were subjected to global assessment of pain, swollen and tender joint counts, and C reactive protein.SUBJECTS: were randomised to etanercept 50 mg weekly (n=373) or 50 mg twice weekly (n=379) for 12 weeks; all subjects then received etanercept 50 mg weekly for 12 weeks. The performance of the scores at baseline and on weeks 12 and 24 was compared between the two treatment regimens.RESULTS: The mCPDAI and DAPSA could distinguish response to treatment comparing baseline and 12-week or 24-week values (p< 0.0001). The mCPDAI, not DAPSA, could distinguish response between the two treatment groups at 12 weeks (p=0.0492), but not at 24 weeks. All domains evaluated contributed to the data variability of the mCPDAI; the most significant were dactylitis (r=0.64) and enthesitis (r=0.60).CONCLUSION: In psoriatic arthritis with severe skin involvement, the mCPDAI was able to distinguish treatment response between the two etanercept doses. DAPSA, while demonstrating improvement in both groups over time, was unable to distinguish response between the different doses of etanercept. Further studies are needed to confirm the sensitivity of both indexes. PMID: 21989542
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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 | 0.014 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.517 | 0.404 |
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".