Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC) is a rare malignancy with a distinct epidemiological pattern and is most often associated with Epstein-Barr virus (EBV). EBV plays a critical role in NPC pathogenesis, with viral proteins driving oncogenesis by altering immune regulation, apoptosis, and tumor progression. The unique molecular landscape of NPC presents both challenges and opportunities for therapeutic development, particularly in the recurrent and metastatic (R/M) setting, where treatment resistance remains a major hurdle. While platinum-based chemotherapy has traditionally been the standard of care for R/M NPC, immune checkpoint inhibitors (ICIs) have emerged as a key component of treatment. However, both intrinsic and acquired resistance to PD-1/PD-L1 blockade underscore the need for alternative strategies, including modulation of alternative immune checkpoints and simultaneous engagement of non-redundant pathways to enhance responses and durability. Leveraging EBV-driven biology, emerging immunotherapeutic approaches, such as EBV-specific adoptive cellular therapies and therapeutic vaccines, aim to induce durable immunity to viral proteins. Additionally, targeted therapies including receptor tyrosine kinase inhibitors, epigenetic modulators, and antibody-drug conjugates are redefining precision medicine by selectively delivering cytotoxic agents to tumors. With growing insights into the biology of NPC and evolving therapeutics, the integration of immunotherapy, targeted agents, and biomarker-driven strategies is poised to transform NPC treatment, emphasizing biology-driven, multimodal approaches to optimize patient outcomes.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".