Liver Discard Rate Attributable to Conservative Estimations of Steatosis: An Inference-based Approach
Bibliographic record
Abstract
BACKGROUND: On-site conservative estimations of steatosis could result in the unnecessary discard of donor livers. This study applied the body mass index as an independent statistical indicator to determine the extent of this problem. We aimed to quantitatively evaluate if decisions based nonbiopsy donor liver assessments are more conservative (inclined to reject marginal fatty livers) than biopsy-based evaluations. METHODS: The study processed intradatabase comparisons using 177 081 datasets from Organ Procurement and Transplantation Network spanning 2004 to 2022 September in the United States. Postmatching body mass index was applied as an independent indicator of statistical risk of hepatic steatosis (HS). RESULTS: A total of 7420, 4990, 5994, and 7523 pair of donors with/without biopsy records were matched in 2004-2010, 2011-2014, 2015-2018, and 2019-2022 September, respectively. Consistent with our hypothesis, absent biopsies and evaluations before and during organ procurement were observed to be more conservative, leading to the discard of 16.4% (2004-2010), 16.9% (2011-2014), 10.6% (2015-2018), and 10.3% (2019-2022 September) of potential donor livers. CONCLUSIONS: The study concludes that there was a significant (10.3%-16.9%) disparity caused by on-site nonbiopsy 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 | 0.062 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".