Peri-operative medical emergency team activation in liver transplantation
Bibliographic record
Abstract
BACKGROUND: The medical emergency team (MET) system functions to promptly identify acutely ill patients at-risk for deterioration. Liver transplant (LT) patients are at-risk for serious post-operative complications. OBJECTIVE: To evaluate the characteristics of MET activations in post-operative LT patients and to compare clinical outcomes with case-matched controls. METHODS: Retrospective case-control study of all adult patients receiving LT over a 3-year period. Cases were defined as post-operative LT patients who received a MET activation. Controls were defined as LT patients who did not receive a MET activation during the same period, and were matched for age, sex and pre-operative Model for End-Stage Liver Disease (MELD) score. RESULTS: We found 10.3% (n=18) of LT patients received a total of 26 MET activations (149.4 per 1000 admissions). The mean (SD) age was 54 (9.5) years and 52% were females. There were no differences in baseline characteristics or underlying liver disease between groups, except pre-operative hepatic encephalopathy was more common among cases (55.6% vs 22.2%, p=0.03). Of the MET activations, respiratory distress was the most common 'trigger' (87.7%). In 42.3% (n=11) of the activations, MET criteria had been fulfilled in the 24 h preceding. In these MET activations, patients were characterised as tachypnoeic, hypoxaemic, hypotensive, tachycardic and/or oligo-anuric. MET patients had significantly longer ICU and hospital lengths of stay, along with greater rate of unplanned ICU re-admission (83.3% vs 13.9%, p<0.0001) compared with controls. MET patients also had higher in-hospital and 1-year post-discharge mortality (p=0.10, <0.001, respectively) compared with controls. CONCLUSIONS: LT patients with post-operative complications prompting MET activation had higher morbidity and mortality compared with controls; however, the MET may have been under-utilised and/or delayed. Further prospective multi-centre investigation is warranted.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".