Bibliographic record
Abstract
Although whole liver tolerance to radiation therapy (RT) is low, hepatocellular carcinoma (HCC) can be treated with focal high-dose RT, using a variety of advanced and specialized treatment strategies. Technical advancements in external beam RT that facilitate the safe delivery of RT to a wide spectrum of patients include conformal RT planning, breathing motion management, and image-guided RT. A variety of doses and RT fractionation schemes have been used safely alone or in combination with other therapies such as transarterial chemoembolization. Charged particles, produced from very specialized treatment units, are associated with particularly desirable dose distributions allowing tumoricidal doses to be delivered with sustained tumor control and little toxicity, even in the presence of Child-Pugh class B or C cirrhosis. Another strategy to deliver RT to HCC is hepatic arterial delivery of radioisotopes, such as microspheres tagged with yttrium-90. Liver toxicity is more likely in patients with reduced liver reserve and/or tumors infiltrating the majority of the liver. Phase II studies and a small phase III trial have demonstrated activity of hepatic arterial radioisotopes in HCC, providing rationale for large confirmatory randomized trials. Recurrences after RT occur most often within the liver, outside the high-dose irradiated volume, and outcomes after RT to very large and/or diffuse HCC are poor, providing rationale for combining RT with other therapies or novel radiation sensitizers. Given the vascular properties of HCC, there is rationale for investigating RT with anti-vascular endothelial growth factor-targeted agents.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".