Nasopharyngeal carcinoma: treatments and outcomes in the 20th century
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC), although rare in Europe and North America, is not uncommon in parts of Asia such as southern China and Hong Kong. Consequently, very few oncologists in the Western world have extensive experience in treating this neoplasm. Treatment using external beam therapy and/or brachytherapy evolved greatly during the 20th century and is still evolving, particularly with the use of adjunctive chemotherapy regimes. Diagnosis of NPC has also improved with the availability of CT and MRI. This worldwide review is divided into historical, transitional and modern eras, with the latter concerning 1971-2000. Currently, the most controversial aspects of NPC are recommendations for treatment of recurrent disease and the role of chemotherapy in the overall framework of treatment. Comparison of results from different centres is not possible without an understanding of the various staging systems that are, and have been, used; a comparison is given in this review. In the future, early diagnosis, adequate radiation dose to the primary with boost to bulky disease, and regular follow-up with biopsy of any suspicious residual or recurrent disease, are likely to become key issues to improve outcome. Also, apart from direct/indirect nasopharyngoscopy, the role of follow-up CT needs to be studied for early detection of residual or recurrent disease. More clinical trials on chemo-radiation are also required, in order to study optimum doses and agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".