Screening for Depression in Systemic Lupus Erythematosus with the British Columbia Major Depression Inventory
Bibliographic record
Abstract
Accurate identification of depression in patients with systemic lupus erythematosus (SLE) is particularly complicated because the vegetative symptoms of depression also reflect core features of this autoimmune disease. Self-reported symptoms in patients with SLE ( n = 103) and community control subjects ( n = 136) were examined with the British Columbia Major Depression Inventory and the Beck Depression Inventory-II. The patients with lupus obtained higher scores on most items of the former inventory. A logistic regression analysis assessed whether a subset of these items were uniquely related to group membership. Clinically significant fatigue was much more common in patients with lupus than in the control group. Two items relating to sleep disturbance also entered the equation as unique predictors. The three-variable model resulted in 85% of the control subjects and 66% of the patients being correctly classified. A subset of patients with depression, according to the Beck inventory (17 or higher), were selected ( n = 41). Their most frequently endorsed symptoms on the British Columbia Inventory were fatigue (90.2%), trouble falling asleep (70.7%), cognitive difficulty (61%), and psychomotor slowing (58.5%). Only 29.3% reported significant sadness. 15% of these subjects were classified as not depressed, 46% as possibly depressed, and 39% as probably depressed on the British Columbia Inventory. It is advisable to assess whether patients are experiencing significant sadness or loss of interest before concluding that a high score on a screening test corresponds to probable depression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".