How "soft" are soft neurological signs? The relationship of subjective neuropsychiatric complaints to cognitive function in systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: As part of a longitudinal study of cognitive function in systemic lupus erythematosus (SLE), we documented the range and frequency of subjective neurologic and/or psychiatric (NP) complaints in Never-NP-SLE patients, and related these to cognitive function, using the latter as a primary indicator of nervous system involvement. METHODS: Thirty patients with SLE who did not have major neurologic and psychiatric involvement underwent baseline and followup neuropsychological testing roughly 5 years apart. Within 0-13 months prior to retesting, each patient completed a 42 item questionnaire recording NP symptoms. RESULTS: The group as a whole endorsed 26% of symptoms. Fourteen patients labelled high endorsers (> 35% of items) endorsed, on average, 42% of symptoms. There was a significant association between higher item endorsement and lower cognitive function (r = -0.46, p < 0.02) and significantly poorer cognitive performance in the high compared to low endorser groups (t = -3.07, p < 0.005). In addition, a subset of 8 items was endorsed at least twice as often by SLE patients as by patients with rheumatoid arthritis (n = 12) or healthy controls (n = 10). CONCLUSION: These results suggest that "minor" NP symptoms and, in particular, a small subset of subjective complaints may be sufficient to raise suspicion of subclinical nervous system involvement in the absence of clinically evident NP-SLE.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".