Serum Bicarbonate and Graft and Patient Outcomes Among Kidney Transplant Recipients: A Retrospective Cohort Study Evaluating Changes in Serum Bicarbonate Over Time
Bibliographic record
Abstract
Rationale & Objective: Identification of treatable risk factors for kidney allograft failure is necessary to improve graft longevity. Metabolic acidosis with either low serum bicarbonate or normal serum bicarbonate (eubicarbonatemic metabolic acidosis) is implicated in native kidney disease progression but its effects in kidney transplant recipients are unclear. Study Design: Retrospective cohort study. Setting & Participants: ) and serum bicarbonate data were used to generate a cohort of kidney transplant recipients with data from ≥1 year before and after transplantation. Primary Predictor: The primary independent variable was a change in serum bicarbonate from baseline. Outcomes: The primary outcomes were graft failure and all-cause mortality. The secondary outcomes were major adverse cardiac events and all-cause hospitalization. Analytical Approach: We used adjusted time-dependent Cox proportional hazards models to assess the risk of graft failure, all-cause mortality, major adverse cardiac events, and time to first hospitalization. Results: In this US community-based cohort of 1,915 kidney transplant recipients with a median follow-up of ∼2.5 years, each 1-mEq/L increase in serum bicarbonate was associated with significantly lower hazard of graft loss, death, major adverse cardiac events, and hospitalization by 10%, 8%, 4%, and 8%, respectively. Limitations: Possible residual confounding. Conclusions: In a US community-based population of kidney transplant recipients, even small incremental increases in serum bicarbonate (1 mEq/L) were significantly associated with reduced hazard of graft loss, all-cause mortality, cardiovascular events, and hospitalization. Interventional trials evaluating the potential benefits of treating metabolic acidosis in kidney transplant recipients are warranted.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".