Prevalence of depression, and subclinical cognitive deficit in patient of SLE: A study from a tertiary health care centre (P9-5.022)
Bibliographic record
Abstract
Objective: Aim: This study aims to determine the prevalence of cognitive deficits in systemic lupus erythematosus (SLE) patients SLE Background: Introduction: Significant number of systemic Lupus Erythematosus (SLE) patients have cognitive impairment and depression, which may or may not be part of Neuropsychiatric SLE. They may be clinical or subclinical, and their prevalence varies according to the tool used. Current literature on its prevalence lacks clarity, especially in the Indian community. Design/Methods: Material and Method: It is a Cross-sectional study. All SLE patients (SLICC criteria) attending the outpatient clinic were evaluated with Hamilton Rating Scale for Depression (HRSD) and Mini-mental state examination (MMSE). Results: 250 patients of SLE attending an OPD clinic of a tertiary care hospital were evaluated. Based on Hamilton Rating Scale for Depression (HRSD), 89.6% of patients were depressed to some extent. Depression was mild in 43% of patients, moderate in 32.6 %, and severe in 14.8%. Based on mini-mental status examination scores, some cognitive impairment was found in 21.6 % of patients. Cognitive impairment was mild in 18.8% and moderate in 2.8 %, while none had severe. Conclusions: Some amount of depression is found in almost every SLE patient, and about half have moderate to severe depression that may require treatment. One-fifth of SLE patients may have a cognitive impairment, and widespread use of diagnostic tools may help in early recognition. Disclosure: Dr. Ramteke has nothing to disclose. Dr. Ramteke has nothing to disclose.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".