#2041 Correlation between protein energy wasting and cognitive impairment in maintenance hemodialysis patients
Bibliographic record
Abstract
Abstract Background and Aims Maintenance hemodialysis (MHD) patients widely exist malnutrition, which can cause severe cognitive impairment (CI), may relate to protein energy wasting (PEW). Our research aimed at exploring the relationship between PEW and CI. Method A total of 160 MHD patients were included and divided into non-CI (nCI) group and CI group. We collected patients’ general information, laboratory data, anthropometric data, diet data, diagnosis and evaluation results of PEW, and montreal cognitive assessment-basic (MoCA-B) score. Independent sample t-test was used to compare clinical characteristics of patients in different groups. Univariate and multivariate logistic regression analysis (MLR) were used to screen the influencing factors of CI. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the constructed model. Results There was significant differences in the detection rate of PEW, prealbumin (PAB), body mass index (BMI), normalized daily protein intake (nDPI), normalized daily energy intake (nDEI), scored patient-generated subjective global assessment (PG-SGA) and malnutrition inflammation score (MIS) score between the nCI and CI groups (P < 0.05). MHD patients merged PEW showed lower scores in MoCA-B total scores, orientation, calculation, abstraction, visual perception and attention compared to MHD patients unmerged PEW (P < 0.05). MLR showed that age, Kt/v, nDPI, MIS score, and PEW were independent influencing factors of CI (P < 0.05). The predictive value of the constructed model based on the aforementioned factors for cognitive impairment was 94.3% (95% CI1, 90.5–98. 1%), the sensitivity and the specificity were 92.4% and 89.7% respectively. The best cut-off for predicting CI was 0.469. Conclusion In MHD patients, PEW is an independent risk factor of CI. The model we build showed great specificity and sensitivity for predicting CI, which can improve the prognosis of patients. 1confidence interval, differentiate from cognitive impairment.
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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".