Liver discard rate due to conservative estimations of steatosis: an inference-based approach
Bibliographic record
Abstract
Abstract Background On-site conservative estimations of steatosis could result in the unnecessary discard of donor livers. This study applied the body mass index (BMI) as an independent statistical indicator to determine the extent of this problem. We explored two hypotheses: (I) that because of varying levels of expertise and protocols (reputational risk for pathologists), biopsies at transplant centers overestimate hepatic steatosis (HS), and (ii) that non-biopsy donor liver assessments are more conservative than biopsy-based evaluations. Methods The study processed cross-database and intra-database comparisons using data from the National Health and Nutrition Examination Survey (NHANES) and Organ Procurement and Transplantation Network (OPTN) spanning January 2017 to March 2020 in the United States. Post-matching BMI was applied as an independent indicator of statistical risk of HS. Results Contrary to our first hypothesis , biopsies at transplant centers did not overestimate HS - biopsy-classified donor livers were found in similar or lower risk categories. Consistent with our second hypothesis , absent biopsies, evaluations before and during organ procurement were observed to be more conservative, leading to the discard of 11.9% (373) of potential donor livers. Conclusions The study concludes that there was a significant (11.9%) disparity caused by on-site non-biopsy assessments of HS, leading to the unnecessary discard of potential donor livers. The findings emphasize the need to develop more accurate intraoperative techniques for assessing HS to optimize donor liver procurement.
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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 | 0.105 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".