Role of liver transplantation for surgical management of malignant liver tumors
Bibliographic record
Abstract
Head and neck adenoid cystic carcinoma (ACC) is a common malignancy often associated with an aggressive clinical course and a wide array of gene mutations. This systematic review aimed to determine the prevalence of these mutations and their association with prognosis and recurrence in ACC. A search of the scientific literature was carried out from inception till 31 July 2024 in the electronic databases – PubMed, EMBASE, Scopus, Web of Science, Ovid/MEDLINE, and Science direct following specific eligibility criteria. The methodological quality of the included studies was assessed using the Newcastle-Ottawa tool. 31 studies were included, and numerous genes like MYB, NOTCH, TP53, PIK3CA, ARID1A, KDM6A, RAS, SPEN, and many more were identified and were related to poor prognosis. Identification of different genes using wide NGS panels and combination of molecular techniques becomes necessary as multiple genes might be involved in ACC pathogenesis and subsequent targeted therapies can be designed.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".