The Clinical Impact of an Early Decline in Kidney Function in Patients Following Heart Transplantation
Bibliographic record
Abstract
Renal dysfunction is a well-known complication following heart transplantation. We examined an early decline in kidney function as a predictor of progression to end-stage renal disease and mortality in heart transplant recipients. We performed a retrospective cohort study of 233 patients who received a heart transplant between July 1985 and July 2004, and who survived >1 month. The decline in estimated creatinine clearance (CrCl) was used to predict the outcomes of need for chronic dialysis or mortality >1-year posttransplant. The earliest time to chronic dialysis was 484 days. A 30% decline in CrCl between 1 month and 12 months predicted the need for chronic dialysis (p = 0.01), all-cause mortality (p < 0.0001) and time to first CrCl </=30 mL/min at >1-year posttransplant (p = 0.02). A 30% decline in CrCl between 1 month and 3 months also independently predicted the need for chronic dialysis (p = 0.04) and time to first CrCl </= 30 mL/min at >1-year posttransplant (p = 0.01). In conclusion, an early drop in CrCl within the first year is a strong predictor of chronic dialysis and death >1-year postheart transplantation. Future studies should focus on kidney function preservation in those identified at high risk for progression to end-stage kidney disease and mortality.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".