i096 Management of central nervous system manifestations of rheumatic diseases
Bibliographic record
Abstract
Central nervous system manifestations of the autoimmune rheumatic diseases are often very challenging to diagnose and manage, especially when the presentation is acute and time is of the essence. Diseases such as systemic lupus erythematosus (SLE), systemic vasculitis and antiphospholipid syndrome (APS) may all have neurological manifestations. In a patient with both SLE and APS, it is often very difficult to identify the primary driver for the patient's presenting clinical features. Even more confusingly, complications from medical conditions such as severe hypertension may cause symptoms such as seizures, which may be mistaken, for example, for cerebral lupus. Identifying demyelinating disorders in rheumatic disorders such as SLE, APS and Sjögren's syndrome at the bedside can be intimidating. This lecture will give a brief overview with clinical examples, of an approach to the diagnosis and management of neurological manifestations of the autoimmune rheumatic diseases. Disclosures: D.D'C. consultancies; GlaxoSmithKline, Eli Lilly, Actelion, NICE. Member of speakers’ bureau; UCB, Eli Lilly.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".