Validity of the Italian algorithm for the attribution of neuropsychiatric events in systemic lupus erythematosus: a retrospective multicentre international diagnostic cohort study
Bibliographic record
Abstract
OBJECTIVE: To validate the Italian algorithm of attribution of neuropsychiatric (NP) events to systemic lupus erythematosus (SLE) in an external international cohort of patients with SLE. METHODS: A retrospective cohort diagnostic accuracy design was followed. SLE patients attending three tertiary care lupus clinics, with one or more NP events, were included. The attribution algorithm, applied to the NP manifestations, considers four weighted items for each NP event: (1) time of onset of the event; (2) type of NP event (major vs minor), (3) concurrent non-SLE factors; (4) favouring factors. To maintain blinding, two independent teams of assessors from each centre evaluated all NP events: the first provided an attribution diagnosis on the basis of their own clinical judgement, assumed as the 'gold standard'; the second applied the algorithm, which provides a probability score ranging from 0 to 10. The performance of the algorithm was evaluated by calculating the area under curve (AUC) of thereceiver operating characteristic curve. RESULTS: The study included 243 patients with SLE with at least one NP manifestation, for a total of 336 events. 285 (84.8%) NP events involved the central nervous system and 51 (15.2%) the peripheral nervous system. The attribution score for the first NP event showed good accuracy with an AUC of 0.893 (95% CI 0.849 to 0.937) using dichotomous outcomes for NPSLE (related vs uncertain/unrelated). The best single cut-off point to optimise classification of a first NPSLE-related event was≥7 (sensitivity 87.9%, specificity 82.6%). Satisfactory accuracy was observed also for subsequent NP events. CONCLUSIONS: Validation exercise on an independent international cohort showed that the Italian attribution algorithm is a valid and reliable tool for the identification of NP events attributed to SLE.
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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 | 0.003 | 0.006 |
| 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 | 0.002 | 0.001 |
| 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".