Relationship between non‐alcoholic fatty liver disease and <scp>MIA</scp> syndrome
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is an important factor in the pathogenesis of cardiovascular diseases in the general population. Recently, it has been shown that NAFLD is highly prevalent in chronic kidney disease (CKD) patients. Ninety-four hemodialysis (HD) patients were followed for a time period of 18 months or until death. Patient's survival rate was determined in relation to their nutritional and inflammatory state, and the presence of NAFLD. We also investigated the association between the presence of NAFLD and the patients' nutritional and inflammatory state. We did not find any significant association between the clinical parameters of nutritional status and the mortality rate. However, the mortality rate was statistically significantly higher in patients with low serum albumin and high high-sensitive C-reactive protein (hs-CRP) levels and in those who had NAFLD. Surprisingly, patients who had received enteral nutrition did not have a better survival rate. The severity of liver steatosis was negatively correlated with the serum albumin levels, while it was positively correlated with hs-CRP values. Furthermore, serum albumin levels showed a negative correlation with hs-CRP levels. We did not find any significant association between the presence of NAFLD and clinical parameters of nutrition. We have shown that NAFLD could be one more possible example of reverse epidemiology in patients undergoing HD. NAFLD may be the missing link that causally ties malnutrition, inflammation, and atherosclerosis syndrome to the morbidity and mortality in patients undergoing HD.
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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) | 0.004 | 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".