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Record W4234144962 · doi:10.1002/0471463736.tnmp13

Nasopharyngeal Carcinoma

2003· other· en· W4234144962 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTNM Online · 2003
Typeother
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsNasopharyngeal carcinomaDiseaseGenetic predispositionCategorizationMedicineHazardEpidemiologyIntensive care medicineRadiation therapyPathologyBiologyComputer scienceInternal medicineArtificial intelligence

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.000

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.

Opus teacher head0.030
GPT teacher head0.315
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it