A comparison of prospective and retrospective evaluations of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index for systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the comparability of prospective and retrospective evaluations of the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SLICC/ACR DI). METHODS: Consecutive patients meeting ACR criteria for SLE were enrolled prospectively in our cohort. Prospective SLICC/ACR DI scores were collected on the 134 cohort members who were observed in the cohort between 1993-1999. The last available prospective SLICC/ACR DI scores were compared to scores that were retrospectively assigned (for the corresponding time point) from chart review by a research nurse blinded to the prospective values. Intra- and inter-observer agreement was assessed. Kappa coefficients with 95% confidence intervals (CI) were determined. RESULTS: The kappa correlation coefficient for agreement between prospective versus retrospective total damage scores was 0.68 (95% CI 0.54-0.81). Moderate to very good agreement was also observed with respect to the 12 individual organ systems itemized in this damage index. Substantial agreement was found between assessments done by different research nurses and for repeat assessments done by the same research nurse. CONCLUSION: These data suggest good agreement between prospective and retrospective evaluations of the SLICC/ACR DI scores.
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 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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".