Reducing length of stay in patients following liver transplantation using the model for continuous improvement
Bibliographic record
Abstract
Length of stay (LOS) is a significant contributor to overall patient outcomes for patients undergoing liver transplantation. This study documents a quality improvement project aiming to reduce the median post-transplant LOS for liver transplant patients. We instituted five Plan-Do-Study-Act cycles with the goal of reducing LOS by 3 days from a baseline median of 18.4 days over 1 year. Balancing measures such as readmission rates ensured any decrease in stay was not associated with significantly increased patient complications. Over the 28-month intervention period and 24-month follow-up period, there were 193 patients discharged from hospital with a median LOS of 9 days. The changes appreciated during quality improvement interventions carried over to sustained improvements, with no significant variability in LOS postintervention. Discharge within 10 days increased from 18.4% to 60% over the study period, with intensive care unit stay decreasing from a median of 3.4-1.9 days. Thus, the development of a multidisciplinary care pathway, with patient engagement, led to improved and sustained discharge rates with no significant differences in readmission rates.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".