Evaluation of minimum-to-severe global and macrovesicular steatosis in human liver specimens: a portable ambient light-compatible spectroscopic probe
Bibliographic record
Abstract
This study presents a portable spectroscopic system compatible with ambient light to assess hepatic steatosis (HS) and macrovesicular steatosis (MaS) in human liver specimens. Traditional assessment methods for MaS are limited, prompting the need for non-invasive alternatives. The study utilized a two-stage approach on thawed snap-frozen liver specimens. Biochemical validation compared fat content from Raman and reflectance intensities with triglyceride (TG) quantifications, while histopathological validation contrasted Raman-derived fat content with pathologist evaluations and an algorithm. Analysis of 16 specimens showed a positive correlation between spectroscopic data and TG quantifications. The Raman system differentiated various degrees of global HS and MaS in an additional 66 specimens. A dual-variable prediction algorithm classified significant discrepancies (≥10%) between algorithm-estimated global HS and pathologist-estimated MaS. This study demonstrates the viability of a portable spectroscopic system for non-invasive HS and MaS assessment to enhance real-time donor liver assessments during recovery to improve transplantation outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".