Impact of Postoperative Continuous Renal Replacement Therapy in Lung Transplant Recipients
Bibliographic record
Abstract
Background. Acute kidney injury (AKI) is a common complication after lung transplant (LTx), and continuous renal replacement therapy (CRRT) is increasingly of use to critically ill patients who have developed AKI. However, the optimal timing or threshold of kidney impairment for which to commence CRRT after LTx has been uncertain. There has also been limited information on the impact of CRRT among LTx recipients (LTRs) introduced in the early posttransplant period on survival, graft function, and renal function. We aimed to review LTRs who developed AKI requiring CRRT postoperatively and followed their long-term outcomes at Tohoku University Hospital (TUH). Methods. Medical records of consecutive patients who underwent LTx at TUH between 2000 and 2018 were reviewed, with follow-up to 2019 inclusive. Results. Although mortality in those who required CRRT (n = 21) was increased versus those who did not require CRRT (n = 85)(P = 0.024), conditional survival beyond 3-month posttransplant was not affected (P = 0.131). Additionally, the cumulative incidence of chronic lung allograft rejection (P = 0.160) and the development of chronic kidney disease (P = 0.757) were not significant between groups. Conclusions. The initiation of CRRT posttransplant may be a useful strategy to preserve cardiac and optimize volume management among critically ill patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".