Post liver transplantation delirium assessment using the CAM-ICU-7 scale: A cohort analysis
Bibliographic record
Abstract
Background: We applied the Confusion Assessment Method (CAM)-Intensive Care Unit (ICU)-7 delirium scale to patients who underwent liver transplant (LT). Methods: Retrospective cohort including patients who underwent LT for cirrhosis admitted to the ICU from June 2013 to June 2016 at the University of Alberta Hospital, Canada. Delirium was assessed using the CAM-ICU-7 scale (0–7 points) twice daily on days one and 3 post LT, with the highest score being considered. Primary endpoint was hospital mortality. Results: Among all patients, 101/150 (67.3%) were men and mean age was 52.4 (SD 11.8) years. On days 1 and 3 post LT, mean CAM-ICU-7 scores were 1.8 (SD 1.3) and 1.6 (SD 1.8), respectively. Therefore, on days 1 and 3 post LT, 38/150 (25.3%) and 26/95 (27.4%) patients had delirium. While delirium on day 3 post LT was associated with higher hospital mortality (11.5% versus 0%; p = 0.019), it was not associated with length-of-hospital stay (29.2 versus 34.4 days; p = 0.36). Following adjustment for APACHEII score, delirium on day 3 post LT was associated with higher odds of hospital mortality (adjusted odds ratio [aOR] 1.89 [95% CI 1.02–3.50]). Following adjustment for Glasgow Coma Scale and mechanical ventilation, serum creatinine was associated with higher odds of delirium on day 3 post LT (aOR 2.02 [95% CI 1.08–3.77]). Conclusions: Using the CAM-ICU-7 scale, delirium was diagnosed in a fourth of patients who underwent LT. Delirium on day 3 post LT was associated with higher odds of hospital 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".