Analysis of the correlation between cognitive impairment and non-traditional risk factors in maintenance hemodialysis patients
Bibliographic record
Abstract
Objective To evaluate the incidence of cognitive impairment(CI) and the correlation between CI and non- traditional risk factors among maintenance hemodialysis(MHD) patients.Methods 140 MHD patients were surveyed in this study.The Montreal cognitive assessment scale(MoCA) was used to make cognitive function score.Cross-sectional survey of age,gender,years of education,disease and complications was done.High sensitivity c-reactive protein(hs-CRP),albumin,homocysteine(Hcy),and hemoglobin were assayed.The patients were then assigned into the CI group or the CI-free groups.The clinical and laboratory data were compared between the two groups.Results CI was detected in 80 patients(57.1%).In the CI group,the score of MoCA was(18.70 ± 2.74),the morbidity of combined diabetic nephropathy was 41.3%,hs-CRP was(13.6 ± 7.6) mg/L,and Hcy was(29.6 ± 6.2) μmol/L.Patients with CI had a significantly higher ratio of age>60,longer period of hemodialysis,higher morbidity of combined diabetic nephropathy and hypertension,and higher level of hs-CRP and Hcy(P<0.05).The education years,hemoglobin and albumin level were significantly lower in the CI group(P<0.05).Logistic regression analysis revealed that age,diabetic nephropathy,hemoglobin,Hcy,and hs-CRP were independent risk factors for CIO.Conclusions The incidence of CI in MHD patients is similar to that reported form other countries.Inflammation,homocysteine and anemia as non-traditional risk factors were relevant with the CI in MHD 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 | 0.000 |
| 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".