New Perspectives in the Assessment of Future Remnant Liver
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In order to achieve microscopic radical resection margins and thus better survival, surgical treatment of hepatic tumors has become more aggressive in the last decades, resulting in an increased rate of complex and extended liver resections. Postoperative outcomes mainly depend on the size and quality of the future remnant liver (FRL). Liver resection, when performed in the absence of sufficient FRL, inevitably leads to postresection liver failure. The current gold standard in the preoperative assessment of the FRL is computed tomography volumetry. In addition to the volume of the liver remnant after resection, postoperative function of the liver remnant is directly related to the quality of liver parenchyma. The latter is mainly influenced by underlying diseases such as cirrhosis and steatosis, which are often inaccurately defined until microscopic examination after the resection. Postresection liver failure remains a point of major concern that calls for accurate methods of preoperative FRL assessment. A wide spectrum of tests has become available in the past years, attesting to the fact that the ideal methodology has yet to be defined. The aim of this review is to discuss the current modalities available and new perspectives in the assessment of FRL in patients scheduled for major liver resection.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it