Serial Change in Echocardiographic Parameters and Cardiac Failure in End-Stage Renal Disease
Bibliographic record
Abstract
Echocardiographic abnormalities are the rule in patients starting dialysis therapy and are associated with the development of cardiac failure and death. It is unknown, however, whether regression of these abnormalities is associated with an improvement in prognosis. As part of a prospective cohort study with mean follow-up of 41 mo, 227 patients had echocardiography at inception and after 1 yr of dialysis therapy. Improvements in left ventricular (LV) mass index, volume index, and fractional shortening were seen in 48, 48, and 46%, respectively. Ninety patients had developed cardiac failure by 1 yr of dialysis therapy. Twenty-six percent of the remaining 137 patients subsequently developed new-onset cardiac failure. The mean changes in LV mass index were 17 g/m(2) in those who subsequently developed cardiac failure compared with 0 g/m(2) among those who did not (P = 0.05). The corresponding values were -8 versus 0% for fractional shortening (P < 0.0001). The associations between serial change in both LV mass index and fractional shortening and subsequent cardiac failure persisted after adjusting for baseline age, diabetes, ischemic heart disease, and the corresponding baseline echocardiographic parameter. Regression of LV abnormalities is associated with an improved cardiac outcome in dialysis patients. Serial echocardiography adds prognostic information to one performed at baseline.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".