Incidence of Gastrointestinal Bleeding After Transesophageal Echocardiography Use in Orthotopic Liver Transplantation
Bibliographic record
Abstract
The risk of upper gastrointestinal bleeding (UGIB) after transesophageal echocardiography (TEE) in patients with high grade esophageal varices (EV) that are undergoing Orthotopic Liver transplantation (OLT) is poorly understood. This was a retrospective single-centre cohort study in all patients that underwent OLT at Queen Elizabeth Hospital Birmingham between September 2016 and September 2018. The primary outcome was to determine the incidence of UGIB in patients that have undergone OLT with EV that received TEE. 401 patients were included in the study, of which 320 (80%) received TEE. The incidence of post-operative UGIB in patients that received TEE was 1.6% (5/320) in the entire cohort: 2.7% (4/149) in patients with no evidence of EV and 0.6% (1/171) in patients with EV. UGIB occurred in 1 patient with grade 2 EV and did not occur in patients with grade 1 or 3 EV. The incidence of UGIB in patients that received TEE was not statistically different to patients that did not: 1.6% (5/320) vs. 3.7% (3/81) p = 0.218. In conclusion, in patients that underwent OLT, intra-operative TEE use was associated with low rates of UGIB, even in cohorts with high grade EV. This suggests that TEE is a relatively safe method of haemodynamic monitoring in patients undergoing OLT.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".