Cardiac Troponin T Predicts Long-Term Outcomes in Hemodialysis Patients
Bibliographic record
Abstract
BACKGROUND: Increased plasma troponin T (cTnT), but not troponin I (cTnI), is frequently observed in end-stage renal failure patients. Although generally considered spurious, we previously reported an associated increased mortality at 12 months. METHODS: We studied long-term outcomes in 244 patients on chronic hemodialysis for up to 34 months, correlating the outcomes to plasma cTnT in routine predialysis samples. In addition, subsequent plasma samples at least 1 year later and within 6 months of data analysis were available in 97 patients and were used to identify patients with increasing plasma cTnT. The endpoints used were death and new or worsening coronary, cerebro-, and peripheral vascular disease and neuropathy. RESULTS: Transplantation occurred more frequently in patients with low initial cTnT: 31%, 13%, and 3% in the groups with cTnT < 0.010, 0.010-0.099, and > or = 0.100 microg/L, respectively. In the same groups, total deaths occurred in 6%, 43%, and 59% and cardiac deaths in 0%, 14%, and 24% of patients. In patients with follow-up samples, the group with increasing cTnT had a significantly increased death (relative risk, 2.0; P = 0.028). The increase was mainly in cardiac and sudden deaths. CONCLUSIONS: Higher plasma cTnT predicts long-term all-cause mortality in hemodialysis patients, even at concentrations < 0.100 microg/L, as does an increasing cTnT concentration over time.
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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 | 0.003 |
| 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".