The Size and Localization of the Liver Haemangioma – Risk Factors for Massive Post-Resection Blood Loss
Bibliographic record
Abstract
Hepatic haemangiomas are common benign liver tumours, often detected through advanced imaging and clinically significant when large or symptomatic. The objective of this study was to determine how tumour size, location, and associated operative factors influence perioperative outcomes, specifically focusing on the risk of massive blood loss. This single-centre retrospective-prospective analysis included 101 patients with cavernous haemangioma who underwent resection or enucleation between 2010 and 2023, with retrospective cases covering surgeries from 2010 to 2020 and prospective cases from 2021 to 2023, evaluating tumour diameter, intraoperative technique, and use of vascular control manoeuvres. The sample showed intraoperative blood loss ranging from 20 ml to 400 ml, with an average of 173.5 ml. Bilateral tumours had the highest mean blood loss (249.167 ml), followed by right-sided lesions (189.286 ml), central lesions (158.571 ml), and left-sided lesions (149.255 ml). Larger tumours correlated positively with blood loss (Pearson correlation 0.333; p=0.001), and an increase of 1 cm in diameter corresponded to an additional 3.744 ml of bleeding. For patients with borderline hemodynamic stability, this additional 3.744 mL of bleeding could exacerbate existing circulatory challenges, potentially requiring more intensive monitoring and interventions to maintain stable hemodynamics during surgery. The Pringle manoeuvre, used in 35% of the operations, was tied to a higher observed average blood loss (223.714 ml) relative to cases without vascular inflow occlusion (146.894 ml). This study refines preoperative risk stratification based on tumour size, localization, and vascular involvement, guiding surgical techniques to minimize intraoperative blood loss in hepatic haemangioma resection.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".