Assessing depression in systemic lupus erythematosus: determining reliable change
Bibliographic record
Abstract
Systemic lupus erythematosus (SLE) can follow an unpredictable course. Clinicians and researchers use various self-report inventories to track aspects of the patient's functioning during the course of the illness (e.g. health status, pain, fatigue, quality of life and psychological status). These self-report inventories are used to measure improvement or deterioration as a function of the natural history of the disease process, or as a function of response to treatment. Proper interpretation of scores derived from these inventories requires an understanding of their psychometric properties, in particular, their reliability. It is important to calculate reliable change difference scores for tests commonly used in rheumatology so clinicians can determine if a change score is a reliable indicator of improvement or deterioration in individual patients (i.e. the change score is not likely to be due to measurement error). The purpose of this article is to illustrate the use of the reliable change difference scores when assessing depression in patients with SLE using the Beck Depression Inventory (BDI).
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".