New insights into the tumour immune microenvironment of nasopharyngeal carcinoma
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC) is unique among head and neck cancers for its strong causative association with Epstein Barr-Virus and high levels of immune infiltration that play a role in pathogenesis. As such, immunotherapy for the treatment of NPC is a promising area of research in the pursuit of improving patient outcomes. Understanding the tumour immune microenvironment (TIME) of NPC is the key to developing targeted immunotherapies and stratifying patients to determine optimal treatment regimens. Recent research has uncovered distinct characteristics of the TIME in NPC as well as important differences between the different disease subtypes; however, reviewing the state of the field reveals a further need for the application of novel techniques like multiplexed hyperspectral imaging and mass cytometry. These techniques can be used to identify spatial, compositional, and functional aspects of the TIME in NPC such as immune cell sociology, novel immune populations, and differences in immune-related signalling pathways in NPC in order to identify clinically relevant characteristics for targeted immunotherapy development and biomarker discovery.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".