Brain magnetic resonance and cognitive function changes in maintenance hemodialysis patients
Bibliographic record
Abstract
Objective To investigate cerebrovascular lesions on maintenance hemodialysis (MHD) patients, including types of cerebrovascular disease, and cognitive function changes. Methods A cross-sectional study was applied. A total of 270 MHD patients at hemodialysis center of Peking Union Medical College Hospital were screened, and finally 117 cases were enrolled. Demographic information, aboratory data, MRI and MRA data were collected and assessed. Cognitive function was evaluated with C-MMSE (Chinese mini mental test examination) and C-MoCA (Chinese montreal cognitive assessment). The related factors were selected by Spearman correlation analysis, multiple linear regression and logistic regression analysis. Results The patients' average age was (56.0±12.5) years, average hemodialysis age was (73.5±60.8) months. Only 5.1% patients had clinical history of cerebral infarction or hemorrhage. Pre-hemodialysis blood pressure was (142.7/80.3±18.2/12.9) mmHg, Post-hemodialysis blood pressure was (130.2/79.1±23.4/14.9) mmHg. A total of 18.8% patients had intra-hemodialysis hypotension, spKt/V was (1.45±0.25). MR results showed that 12.0% patients had cerebral artery stenosis, 5.1% patients had cortical infarcts, 39.3% patients had lacunar infarcts, 47.0% patients had microbleeds, 7.7% patients had chronic hematoma, 52.1% patients had abnormal brain whiter matter lesions (WMLs). In cognitive function evaluation, 20.9% patients had abnormal C-MMSE scores, but 65.2% patients had abnormal C-MoCA results. Multiple linear regression showed age (b=0.059, P<0.01), dialysis age (b=0.005, P<0.05) were associated with WMLs in MHD patients. Intra-hemodialysis hypotension was an independent risk factor of lacunar infarcts (b=2.123, P<0.01) and microbleeds (b=3.531, P<0.01). Low serum albumin level was an independent risk factor of cognitive decline (b=0.314, P<0.05). Logistic regression analysis showed pre-hemodialysis systolic blood pressure was an independent risk factor of cortical infarcts [OR=1.088, 95%CI (1.018-1.152), P<0.05]. Gender, dialysis age and pre-dialysis serum TCO2 level were related with chronic hematoma. Conclusions WMLs is related with dialysis voltage. Lacunar infarcts and mirobleeds are related with intra-hemodialysis hypotension. Lacunar infarcts, WMLs and nutritional status are contributed to decline of cognition in MHD patients. Key words: Renal dialysis; Cerebrovascular disorders; Neurobehavioral manifestations
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".