Item weightings for the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Disease Damage Index using Rasch analysis do not lead to an important improvement.
Bibliographic record
Abstract
OBJECTIVE: To develop Systemic Lupus International Collaborating Clinics/American College of Rheumatology Disease Damage Index (SLICC/ACR-DI) item weightings that improve the ability of the measure to predict patient mortality in systemic lupus erythematosus (SLE). METHODS: Disease damage was measured for 738 patients followed at the University of Toronto Lupus Clinic since diagnosis. Using Rasch analysis, item weightings were determined and tested for their ability to predict death in a logistic regression model. Receiver operating characteristic (ROC) curves were produced to compare the original and weighted scales' ability to discriminate patients that died during the followup period from those who remained alive. RESULTS: The average SLICC/ACR-DI score per patient was 1.66. In total, 138 of the patients died during a mean followup of 9.2 years. A Rasch analysis derived weighting scheme using weighted domain scores (SLICC/ACR-DI-weighted) was the best weighted scale, with item reliability = 94%, model mean square infit = 1.01 (STD = 0.05); model mean square outfit = 0.99 (STD = 0.3), separation 4.08. The SLICC/ACR-DI-weighted was modestly better than the SLICC/ACR-DI in discriminating patients who died from those who remained alive. Using standardized scores for comparability, the SLICC/ACR-DI-weighted was better in predicting patient death than the unweighted SLICC/ACR-DI [OR(death)(SLICC/ACR-DI-weighted) = 1.7 vs OR(death)(SLICC-ACR-DI) = 1.4; p < 0.005]. ROC curve analysis supports that the SLICC/ACR-DI-weighted was somewhat superior to the SLICC/ACR-DI for predicting mortality. CONCLUSION: In this test set, the SLICC/ACR-DI-weighted was modestly better in predicting death than the traditional unweighted SLICC/ACR-DI. However, the SLICC/ACR-DI-weighted is more difficult to apply and the weightings appear not to have provided a clinically relevant improvement of the SLICC-ACR-DI.
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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 | 0.098 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".