Left Ventricular Mass in Dialysis Patients, Determinants and Relation with Outcome. Results from the COnvective TRansport STudy (CONTRAST)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Left ventricular mass (LVM) is known to be related to overall and cardiovascular mortality in end stage kidney disease (ESKD) patients. The aims of the present study are 1) to determine whether LVM is associated with mortality and various cardiovascular events and 2) to identify determinants of LVM including biomarkers of inflammation and fibrosis. DESIGN SETTING PARTICIPANTS & MEASUREMENTS: Analysis was performed with data of 327 ESKD patients, a subset from the CONvective TRAnsport STudy (CONTRAST). Echocardiography was performed at baseline. Cox regression analysis was used to assess the relation of LVM tertiles with clinical events. Multivariable linear regression models were used to identify factors associated with LVM. RESULTS: Median age was 65 (IQR: 54-73) years, 203 (61%) were male and median LVM was 227 (IQR: 183-279) grams. The risk of all-cause mortality (hazard ratio (HR) = 1.73, 95% CI: 1.11-2.99), cardiovascular death (HR = 3.66, 95% CI: 1.35-10.05) and sudden death (HR = 13.06; 95% CI: 6.60-107) was increased in the highest tertile (>260 grams) of LVM. In the multivariable analysis positive relations with LVM were found for male gender (B = 38.8±10.3), residual renal function (B = 17.9±8.0), phosphate binder therapy (B = 16.9±8.5), and an inverse relation for a previous kidney transplantation (B = -41.1±7.6) and albumin (B = -2.9±1.1). Interleukin-6 (Il-6), high-sensitivity C-reactive protein (hsCRP), hepcidin-25 and connective tissue growth factor (CTGF) were not related to LVM. CONCLUSION: We confirm the relation between a high LVM and outcome and expand the evidence for increased risk of sudden death. No relationship was found between LVM and markers of inflammation and fibrosis. TRIAL REGISTRATION: Controlled-Trials.com ISRCTN38365125.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".