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Record W2176283489 · doi:10.1002/9780471420194.tnmm39

General Principles of Head and Neck Cancer Management

2017· other· en· W2176283489 on OpenAlexaff
Mitali Dandekar, Anil D′Cruz, Shao Hui Huang, Brian O’Sullivan, Jan B. Vermorken

Bibliographic record

VenueTNM Online · 2017
Typeother
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsArecaMedicineParanasal sinusesLarynxHead and neckCancerHead and neck cancerEtiologyDermatologyOncologyPathologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Summary Head and neck cancers are a global health problem, but particularly in South‐East Asia. These are a heterogeneous group of tumours which include squamous cell carcinoma (or its variants) of the oral cavity, oropharynx, nasopharynx, paranasal sinuses, larynx and hypopharynx. Important aetiological agents are tobacco, areca nut and alcohol. Human papillomavirus (HPV) is emerging as a major factor in the West, predominantly in relationship to cancers of the oropharynx. Nasopharyngeal cancers have been attributed to Epstein‐Barr virus (EBV) in endemic areas. Despite a variety of subsites included under the head and neck cancer rubric, the broad principles of management are essentially the same, although they are profoundly influenced by the anatomical imperative of preserving form and function, as well as disease control. This chapter focuses on these principles. Specific issues are covered in respective chapters.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.039
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0390.025

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 head0.059
GPT teacher head0.373
Teacher spread0.314 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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