Systemic lupus international collaborating clinics renal activity/response exercise: Development of a renal activity score and renal response index
Bibliographic record
Abstract
OBJECTIVE: To develop a measure of renal activity in systemic lupus erythematosus and use it to develop a renal response index. METHODS: Abstracted data from the medical records of 215 patients with lupus nephritis were sent to 8 nephrologists and 29 rheumatologists for rating. Seven nephrologists and 22 rheumatologists completed the ratings. Each physician rated each patient visit with respect to renal disease activity (none, mild, moderate, or severe). Using the most commonly selected rating for each patient as the gold standard, stepwise regression modeling was performed to identify the variables most related to renal disease activity, and these variables were then used to create an activity score. This activity score could then be applied to 2 consecutive visits to define a renal response index. RESULTS: The renal activity score was computed as follows: proteinuria 0.5-1 gm/day (3 points), proteinuria 0.5-1 gm/day = 3 points, proteinuria >1-3 gm/day = 5 points, proteinuria >3 gm/day = 11 points, [corrected] urine red blood cell count > = 5/hpf = 3 points, [corrected] urine white blood cell count > or = 5/hpf = 1 point. [corrected] The chance-adjusted agreement between the renal response index derived from the activity score applied to the paired visits and the plurality physician response rating was 0.69 (95% confidence interval 0.59-0.79). CONCLUSION: Ratings derived from this index for rating of renal response showed reasonable agreement with physician ratings in a pilot study. The index will require further refinement, testing, and validation. A data-driven approach to create renal activity and renal response indices will be useful in both clinical care and research settings.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".