#3517 BRAIN MAGNETIC RESONANCE IMAGING AND COGNITIVE FUNCTION IN PATIENTS RECEIVING HEMODIALYSIS
Bibliographic record
Abstract
Abstract Background and Aims Chronic kidney disease is high risk to adverse events including stroke, cardiovascular diseases and cognitive decline. Cognitive function was known to be linked to chronic kidney disease. The Fazefa's scale, which was used for assessing white matter hyperdensities(WMH), has been reported to associate with poor cognitive performance. We aim to investigate the associations between mini-mental status examination (MMSE), Montreal cognitive assessment (MoCA), cognitive abilities screening instrument (CASI) and Fazefa's scale in patients under hemodialysis (HD). Method The periventricular (PV) and deep white matter (DWM) lesions in brain MR images of 59 patients under dialysis for at least 90 days were graded by Fazefa's scale. Cognition function tests including MMSE, MoCA, and CASI were performed. Multivariable ordinal regression and logistic regression were used for identifying the associations between cognitive performance and Fazekas scale. Results Inverse associations were found between three cognitive function tests across the Fazekas scale of PV lesions (p = 0.037, 0.006 and 0.008 for MMSE, MoCA, and CASI, respectively), and higher scales were associated with lower cognitive function scores in trend; however, the association attenuated in the DWM group. In subdomains of CASI, significant differences were identified in five subdomains, including short-term memory, mental manipulation, abstract thinking, spatial construction, and name fluency; nevertheless, in DWM hyperdensities, only abstract thinking and short-term memory showed obvious correspondence. Conclusion Inverse correlation between the Fazefa's scale, predominantly in PV lesions, and cognition was detected in HD patients. The Fazefa's scale of PV lesions were associated with cognitive performance assessed by MMSE, MoCA and CASI, as well as with subdomains of CASI such as memory, language, and name fluency in HD patients.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".