Acute care and emergency general surgery in patients with chronic liver disease: how can we optimize perioperative care? A review of the literature
Bibliographic record
Abstract
The increasing prevalence of advanced cirrhosis among operative candidates poses a major challenge for the acute care surgeon. The severity of hepatic dysfunction, degree of portal hypertension, emergency of surgery, and severity of patients' comorbidities constitute predictors of postoperative mortality. Comprehensive history taking, physical examination, and thorough review of laboratory and imaging examinations typically elucidate clinical evidence of hepatic dysfunction, portal hypertension, and/or their complications. Utilization of specific scoring systems (Child-Pugh and MELD) adds objectivity to stratifying the severity of hepatic dysfunction. Hypovolemia and coagulopathy often represent major preoperative concerns. Resuscitation mandates judicious use of intravenous fluids and blood products. As a general rule, the most expeditious and least invasive operative procedure should be planned. Laparoscopic approaches, advanced energy devices, mechanical staplers, and topical hemostatics should be considered whenever applicable to improve safety. Precise operative technique must acknowledge common distortions in hepatic anatomy, as well as the risk of massive hemorrhage from porto-systemic collaterals. Preventive measures, as well as both clinical and laboratory vigilance, for postoperative hepatic and renal decompensation are essential.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".