Bibliographic record
Abstract
Abstract Nasopharyngeal carcinoma (NPC) is unusual in several ways. Its epidemiology, associated with ethnicity, genetic predisposition, viral, and environmental dietary exposure, is unique in itself. In addition, the predilection for certain geographic areas, with relative sparing of adjacent regions is noteworthy. Unjustly perhaps, NPC poses a formidable public health hazard to countries that are relatively compromised in their ability to provide the technical diagnostic and therapeutic approaches considered to be necessary for optimal management today. However, what sets it apart from most diseases is the anatomic challenge it presents to the oncology team because of the proximity of the nasopharynx to critical anatomic structures and the high predilection for distant metastasis once the primary and regional lymph‐node areas are extensively involved, which is all too frequent. In this chapter we discuss prognostic factors of importance in the management of NPC using the classification proposed earlier in this book. Factors will be considered by whether they relate to the disease itself, the patient (or host ), or the environment which influences the opportunity for optimal treatment and diagnosis. The classification may not always apply since, in the case of NPC, there may be overlap among factors and arbitrary placement of factors may be necessary. We will also attempt to categorize the available evidence into factors which are essential to our ability to treat the disease ( essential factors), those which add valuable information about the disease but do not affect treatment decisions ( additional factors), and finally those that are being described and may provide important understanding of disease behavior and therapeutic approaches in future years. In the interest of relevance to the treatment of the disease, and for brevity, the discussion of the final group of factors (those termed new and promising ), will be restricted to experience of patient outcome. Therefore, preclinical studies will not receive attention. Special attention to the classification of stage of disease will be given. This is because for NPC anatomic features are so important that a relevant and reproducible system of classification merits attention above other prognostic factors. In fact, few diseases received the same level of attention in the preparation of the 5th edition (TNM) stage classification of the International Union Against Cancer (UICC) and the American Joint Committee on Cancer (AJCC). A major revision of the NPC stage was accomplished by a substantial collaborative consultation among radiation oncologists in Southeast Asia, with support from diagnostic radiologists, pathologists, and surgeons there and elsewhere.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.082 | 0.053 |
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".