A Canadian Perspective on Systemic Therapy for Recurrent or Metastatic Nasopharyngeal Carcinoma
Bibliographic record
Abstract
Although the majority of patients with nasopharyngeal carcinoma (NPC) present with early-stage or locoregional disease that can be treated with definitive radiotherapy, approximately 20% of patients experience disease recurrence, and 15% present with metastatic disease that is not amenable to curative therapy. Management of patients with recurrent or metastatic (r/m) NPC who are not candidates for local salvage therapy is challenging in Canada, as there is uncertainty in extrapolating evidence that is largely generated from Southeast China to non-endemic regions such as Canada. Currently, treatment options in Canada are limited to chemotherapy regimens that can only achieve short-term response and prolongation of survival. The addition of anti-PD-1 monoclonal antibodies to chemotherapy has been shown to extend progression-free survival in recurrent r/m NPC compared to chemotherapy alone; however, approval of PD-1 inhibitors in Canada has lagged behind other jurisdictions where NPC is non-endemic. This paper reviews the current systemic treatment landscape for r/m NPC in Canada, highlights unmet treatment needs for patients who are not candidates for curative therapy, and discusses the challenges and opportunities that lie in emerging therapies.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".