Bibliographic record
Abstract
The treatment of liver cancer by transplantation has evolved into a process of selecting early stage tumors that have a high likelihood of cure. Carefully selected cirrhotic patients with early hepatocellular cancer (< or = 5 cm. diameter and single; < or = 3 cm. diameter if multiple and 3 or fewer lesions; no vascular invasion) have 5-year actuarial survival rates of approximately 75% after transplantation. Preoperative imaging should be as extensive as necessary to accurately define the characteristics of tumor size, location, and number and exclude signs of extrahepatic involvement. Adjuvant and neoadjuvant chemotherapy became part of treatment protocols in many centers at the same time that more stringent criteria for transplant candidacy were applied to patients with cancer, making it difficult to attribute improved results to the chemotherapy. Nevertheless, neoadjuvant chemoembolization for hepatocellular cancer is logical for patients who may wait long periods before receiving transplants. The fibrolamellar variant of hepatoma is a less aggressive tumor and patients can do well after transplantation, but late recurrences are common. Hepatoblastoma in children can respond very favorably to chemotherapy combined with transplantation. Cholangiocarcinoma remains a dreadful malignancy. The rare cases of insitu cholangiocarcioma in patients who receive transplants for sclerosing cholangitis can be cured, but known cholangiocarcinoma has an exceedingly high rate of recurrence after transplantation alone. Recent work combining chemotherapy and radiation with transplantation has not had dramatic success at improving cure rates. Patients with metastatic neuroendocrine tumors of the liver can receive good palliation by transplantation, but the majority of patients eventually develop recurrent cancer.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".