Bibliographic record
Abstract
Hepatic vein stenosis is a rare but serious complication following liver transplantation. Multiple modalities can be utilized to image the hepatic vasculature. Magnetic resonance venography (MRV) provides certain advantages over ultrasound, computed tomography angiography and digital subtraction venography. MRV utilizes the same imaging principles of magnetic resonance angiography in order to image the venous system. Blood pool contrast agents, specifically gadofosveset trisodium, allow for steady state imaging up to 1 h following injection, with improved visualisation of vital venous structures by utilising delayed steady state imaging. Additionally, the inherent physics properties of magnetic resonance imaging also provide excellent soft tissue detail and thus help define the extent of complications that often plague the post-liver transplant patient. This case report describes the use of gadofosveset trisodium in a patient with hepatic venous stenosis following liver transplantation. Initial venography failed to outline the stenoses and thus MRV using a blood pool contrast agent was utilised in order to delineate the anatomy and plan a therapeutic endovascular procedure.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".