Mild cognitive impairment in patients with systemic sclerosis and features analysis
Bibliographic record
Abstract
OBJECTIVE: Nervous system damage in patients with SSc has recently attracted attention. In this study, we aimed to explore mild cognitive impairment (MCI) in SSc patients and the characteristics of these patients. METHODS: A total of 103 SSc patients were consecutively enrolled from July 2018 to May 2019, and 97 matched healthy individuals were also included as controls. Brief cognitive tests, such as the Beijing version of the Montreal Cognitive Assessment (MoCA-BJ), were used to assess the cognitive function of all subjects. We compared the differences in MCI between SSc patients and healthy controls, as well as the differences in demographic and clinical features between SSc patients with and without MCI. Associations of quantitative demographic and clinical features with MoCA-BJ scores in the SSc patients were also evaluated. RESULTS: The score of MoCA-BJ was lower in the SSc group compared with those in the healthy group [24 (9-30) vs 26 (15-30), P < 0.001]. MCI (MoCA-BJ score ≤ 25) was found in 61.2% (63/103) of the enrolled SSc patients but only in 27.8% (27/97) of the healthy individuals. Other tests evaluating some of the specific domains of cognitive functions showed that the SSc patients had impaired memory, attention and executive ability. Compared with SSc patients without MCI, SSc patients with MCI had lower education level, total serum protein and serum albumin but higher ANA positivity. CONCLUSION: MCI is common in patients with SSc and should be drawn to the attention of rheumatologists. Lower education level, malnutrition and higher ANA positivity were closely related to the cognitive dysfunctions in SSc patients, providing directions for further interventions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".