Inverse association between docosahexaenoic acid and mortality in patients on hemodialysis during over 10 years
Bibliographic record
Abstract
We have previously conducted a cohort study to investigate n-3 polyunsaturated fatty acids (PUFAs) in red blood cells (RBCs) and risk of all-cause mortality in hemodialysis (HD) patients over 5 years and found that n-3 PUFAs, especially docosahexaenoic acid (DHA), might be an independent predictor of all-cause mortality. In the present study, we extended the study for another 5 years to determine whether DHA levels in RBCs still predict the mortality of HD patients during a 10-year study period. The study cohort consisted of 176 patients (64.1 ± 12.0 [mean ± standard deviation] years of age, 96 men and 80 women) under HD treatment. The fatty acid composition of patients' RBCs was analyzed by gas chromatography. During the study period of 10 years, 97 deaths occurred. After adjustment for 10 confounding factors, the hazard ratio of all-cause mortality of the HD patients in the highest DHA tertile (>8.1%) was 0.52 (95% confidence interval 0.30-0.91) compared with those in the lowest DHA tertile (<7.2%). However, other n-3 PUFAs such as eicosapentaenoic acid and docosapentaenoic acid (n-3) did not reveal any significant correlations. The level of DHA in RBCs could be an independent predictor of all-cause mortality in HD patients even during a long period of follow-up.
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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 | 0.000 | 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 | 0.001 |
| 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".