Proposed Management Algorithm for Severe Hypoxemia After Liver Transplantation in the Hepatopulmonary Syndrome
Bibliographic record
Abstract
The hepatopulmonary syndrome (HPS) is defined as the triad of liver disease, intrapulmonary vascular dilatation, and abnormal gas exchange, and is found in 10-32% of patients with liver disease. Liver transplantation is the only known cure for HPS, but patients can develop severe posttransplant hypoxemia, defined as a need for 100% inspired oxygen to maintain a saturation of ≥85%. This complication is seen in 6-21% of patients and carries a 45% mortality. Its management requires the application of specific strategies targeting the underlying physiologic abnormalities in HPS, but awareness of these strategies and knowledge on their optimal use is limited. We reviewed existing literature to identify strategies that can be used for this complication, and developed a clinical management algorithm based on best evidence and expert opinion. Evidence was limited to case reports and case series, and we determined which treatments to include in the algorithm and their recommended sequence based on their relative likelihood of success, invasiveness, and risk. Recommended therapies include: Trendelenburg positioning, inhaled epoprostenol or nitric oxide, methylene blue, embolization of abnormal pulmonary vessels, and extracorporeal life support. Availability and use of this pragmatic algorithm may improve management of this complication, and will benefit from prospective validation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".