Predicting Early Extubation After Liver Transplantation: External Validation and Improved Generalizability of a Proposed Fast-track Score
Bibliographic record
Abstract
BACKGROUND: Early extubation of liver transplantation recipients is a cornerstone of fast-track (FT) pathways. Identifying suitable candidates has previously been accomplished using perioperative variables to develop a FT probability score. The objective of this study was to externally validate a proposed FT score. METHODS: Following Research Ethics Board approval, data were extracted on liver transplants conducted at a single center from 2009 to 2017. Data extracted included patient characteristics, intraoperative variables, and postoperative outcome variables. The proposed FT score utilized 9 variables: age, gender, body mass index, model of end-stage liver disease, retransplant, preoperative hospital admission, blood transfusion, operative time, and vasopressor use. We calculated the FT score in our cohort, and assessed the discrimination and calibration of the model. Score performance was explored by subgroup analyses, customization and altering the outcome definition. RESULTS: The FT score was found to predict higher rates of successful FT than was observed in the external cohort (n = 1385) and had reduced discrimination (area under the receiver operating curve, 0.711; 95% confidence interval, 0.682-0.741) compared with the original internal validation cohort (area under the receiver operating curve, 0.830; 95% confidence interval, 0.789-0.871; P < 0.0001). Discrimination was improved by customizing the transfusion (P < 0.0001) components of the simplified score or by level 1 customization of all regression model coefficients (P < 0.0001). A time-based definition of FT (early extubation) did not alter the accuracy of the prediction score (P = 0.914), improving the model's generalizability. CONCLUSIONS: The proposed FT score may help identify patients suitable for early extubation and FT pathways after liver transplantation in conjunction with clinical judgment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".