Proceedings of the 7th Biannual International Symposium on Nasopharyngeal Carcinoma 2015
Bibliographic record
Abstract
The international geographic distribution of NPC is unique among malignancies.Incidence rates can exceed 20 per 100,000 personyears among males and 10 per 100,000 person-years among females in southern China.Intermediate rates are observed in Southeast Asia, North Africa, the Middle East, and the Arctic, as well as among Asian and Pacific Islander migrant populations, while rates generally remain below 1 per 100,000 throughout the rest of the world.The agespecific incidence rate peaks at around 45-59 years in high-incidence areas, whereas a small early incidence peak at ages 15-19 years is followed by a later peak at around 65-79 years in low-incidence areas.The male-to-female incidence ratio is consistently around 2-3 or greater.Much of the striking geographic variation may be attributable to certain risk factors, such as certain HLA alleles and consumption of Chinese-style salted fish, whose geographic distribution mirrors that of NPC.Other NPC risk factors, such as EBV infection, are ubiquitous, yet are almost certainly modified by geographically heterogeneous co-factors.Most established risk factors for NPC are associated with undifferentiated NPC, although tobacco smoking is more strongly associated with squamous cell NPC.The recent worldwide decline in NPC incidence, especially in high-incidence regions, points to an important role of modifiable environmental risk factors that could serve as targets for further disease prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.098 | 0.060 |
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".