The development of a simple questionnaire to screen patients with SLE for the presence of neuropsychiatric symptoms in routine clinical practice
Bibliographic record
Abstract
AIM: The creation of a physician-administered questionnaire to screen patients with Systemic Lupus Erythematosus (SLE) for the presence of symptoms suggestive of neuropsychiatric involvement (NPSLE). METHODS: The development of the questionnaire followed three phases. First, a list of manifestations was prepared based on the ACR case definitions for NPSLE. A first questionnaire was constructed including 119 items. To reduce their number, a Delphi analysis was carried out and a second questionnaire with 62 questions was developed. This questionnaire was administered to 139 patients with SLE (58 with NPSLE: 29 active, 29 inactive; and 81 without NPSLE: 39 active, 42 inactive). Questions relevant to the screening of patients were selected on the basis of the receiver operating characteristic (ROC) curve analysis. RESULTS: Twenty-seven questions concerning central nervous system and psychiatric manifestations were found to be relevant; the remaining could be eliminated without significantly affecting AUC. The area under the ROC curve (AUC) was 0.69 (95% CI 0.61-0.78). A score above 17 was considered as suggestive of the presence of NPSLE with a sensitivity of 92.9% (95% CI 85.1-97.3 %) and specificity of 25.4% (95% CI 14.7-39.00 %). CONCLUSIONS: This questionnaire could represent a 'core set' of questions that could help in clinical practice to identify patients with neuropsychiatric symptoms requiring further evaluation.
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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 | 0.021 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".