Longitudinal Evaluation of Delirium in Liver Transplant Recipients
Bibliographic record
Abstract
Background: Increasingly shorter intensive care unit stays have limited the screening for delirium and therefore our understanding of delirium risk and characteristics in the modern liver transplant population. Objective: We sought to evaluate delirium prevalence in the intensive care unit, on the surgical floor, and overall during the liver transplant hospitalization. Methods: We enrolled 50 liver transplant recipients to receive daily delirium assessments using both the 4AT and either the Confusion Assessment Method (CAM) for the ICU or the CAM short form immediately pre-transplant and until the first of discharge or post-transplant day 14. We evaluated delirium prevalence in the ICU, on the surgical floor, and overall and visualized the daily mental status of recipients and cumulative incidence of delirium in this cohort. Results: Delirium was present in 58% of recipients, including 47% in the ICU and 43% on the surgical floor. Nine participants (31%) were captured as having delirium only on the surgical floor. Demographic and transplant characteristics were statistically similar between recipients with and without post-transplant delirium. Compared to recipients without post-transplant delirium, recipients with delirium had similar ICU length of stay [3 (2-5) vs. 2 (1-3) days, p=0.24] but significantly longer transplant hospitalization length of stay [16 (13-20) vs. 8 (8-13), p=0.004]. Conclusions: Delirium is highly prevalent among liver transplant recipients not only during their stay in the intensive care unit, but also on the surgical floor. These findings underscore the importance of continuing routine assessment for delirium on liver transplant recipients after transfer to the surgical floor to optimize post-transplant care.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".