Establishing targets for goal-directed anesthesia in renal transplantation: A cohort analysis of high-saliency surgical time courses
Bibliographic record
Abstract
Delayed graft function (DGF) increases morbidity and mortality in kidney transplant recipients. Operative parameters, including hemodynamic manipulation through vasopressors and fluids, can impact perfusion to the newly transplanted kidney and influence DGF incidence. We analyzed intraoperative time-series data in 5-minute intervals from kidney transplant recipient operations (N = 545) in conjunction with pretransplant characteristics and postsurgical outcomes, including DGF incidence, 60-day creatinine, and graft survival. Of the operations, 127 DGF events were captured in our cohort from a single academic transplant center (57/278 donations after brainstem death [DBDs], 65/150 donations after circulatory/cardiac death [DCDs], 5/117 live donations). In multiple regression, postanastomosis hypotension defined as mean arterial pressure (MAP) <75 mmHg was a risk factor for DGF independent of conventional predictors of DGF in DCD and DBD kidneys. DCD recipients with DGF had lower average postanastomosis MAP (DGF: 80.1 ± 8.1 mmHg vs no DGF: 76.4 ± 6.7 mmHg, P = .004). Interaction analysis demonstrated above-average doses of vasopressors and crystalloids were associated with improved outcomes when used at MAPs ≤75 mmHg, but they were associated with increased DGF at MAPs >75 mmHg, suggesting that the incidence of DGF can be highly influenced by intraoperative hemodynamic controls. This analysis of surgical time courses has identified potential new strategies for goal-directed anesthesia in renal transplantation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".