Left ventricular mass index and aortic arch calcification score are independent mortality predictors of maintenance hemodialysis patients
Bibliographic record
Abstract
To analyze predictive factors for all-cause mortality, cardiovascular (CV) mortality, nonfatal CV events (CVE) in maintenance hemodialysis (MHD) patients, and to compare the effects of standard hemodialysis (HD) and online hemodiafiltration (HDF) on these factors and outcomes. A total of 333 MHD patients were prospectively followed up for 50 ± 15 months and all-cause death, CV death and CVE were registered. At the baseline, demographic, clinical, and laboratory data of the whole population were recorded. Then, patients were stratified into two groups according to the dialysis modalities, HD (n = 268) and HDF (n = 65). At the end of 6th month, clinical and laboratory data were recorded again. The predictive factors at baseline for all-cause mortality, CV mortality, and CVE were analyzed by Cox regression. The effects of HD and HDF on these factors at the 6th month and long-term outcomes were compared by t-test and Kaplan-Meier method, respectively. Age, gender, left ventricular mass index (LVMI), aortic arch calcification score (AoACS), hemoglobin (Hb) <10 g/dL, and ferritin >500 ng/mL maintained independent associations with all-cause mortality. C-reactive protein (CRP), LVMI, AoACS, and Hb <10 g/dL were associated with CV mortality. Prior cardiovascular disease (CVD), AoACS and LVMI were independent predictors of nonfatal CVE. Higher body mass index (BMI), body weight, total serum cholesterol, Hb concentration, and lower CRP level, LVMI, and AoACS were found in patients on HDF at the end of the 6th month. Improved outcomes with longer survival time for all-cause mortality, CV mortality, and CVE were found in HDF group. Age, gender, LVMI, AoACS, Hb, and ferritin were predictors of all-cause mortality in MHD patients. CRP, LVMI, AoACS, and Hb were associated with CV mortality. Prior CVD, AoACS, and LVMI were independent predictors of nonfatal CVE. HDF could improve BMI, body weight, total serum cholesterol, Hb, CRP, LVMI, AoACS, and long-term outcomes, including all-cause mortality, CV mortality, and CVE.
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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".