Prediction of poor graft function by means of gastric tonometry in patients undergoing liver transplantation
Bibliographic record
Abstract
INTRODUCTION. Splanchnic hypoperfusion appears to play a key role in the failure of functional recovery of the graft after orthotopic liver transplantation (LT). The aim of this study was to determine if alterations of tonometric parameters, which are related to splanchnic perfusion, could predict poor graft function in patients undergoing LT. MATERIALS AND METHODS: After Ethics Committee approval, 68 patients undergoing LT were enrolled. In all the patients, regional-arterial CO2 gradient (Pr-aCO2) was recorded; in addition, the difference between Pr-aCO2 recorded at anhepatic phase (T1) and at the end of surgery (T2) (T2- T1 = ΔPr-aCO2) was calculated. Poor graft function was determined on the basis of Toronto's classification 72 hours after LT. Student t-test and logistic regression analysis were used for statistical purpose. Results. ΔPr-aCO2 was significantly greater in patients with poor graft function (3.5 ± 13.2) compared to patients with good graft function (-5.8 ± 12.3) (p = 0.014). The logistic regression analysis showed that the ΔPr-aCO2 was able to predict the onset of poor graft function (p = 0.037). A value of ΔPr-aCO2 ≥ -4 was associated with poor graft function with a sensibility of 93.3% and a specificity of 42.3%. CONCLUSION. Our study suggests that the change of Pr-aCO2 may be a valuable index of graft dysfunction. Gastric tonometry might give early prognostic information on the graft outcome, and it may aid clinicians in planning a more strict follow-up and proper interventions in order to improve graft survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".