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Record W2167588859 · doi:10.1586/14737140.2015.978862

Imaging evaluation of lymphadenopathy and patterns of lymph node spread in head and neck cancer

2014· review· en· W2167588859 on OpenAlex
Reza Forghani, Eugene Yu, Mark Levental, Peter M. Som, Hugh D. Curtin

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

VenueExpert Review of Anticancer Therapy · 2014
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkMcGill UniversityUniversity of TorontoJewish General Hospital
Fundersnot available
KeywordsMedicineHead and neck cancerRadiologyLymph nodeCervical lymph nodesHead and neckCervical lymphadenopathyCancerRadiation therapyPathologyMetastasisDiseaseSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Accurate and consistent characterization of metastatic cervical adenopathy is essential for the initial staging, treatment planning and surveillance of head and neck cancer patients. While enlarged superficial nodes may be clinically palpated, imaging allows identification of deeper adenopathy as well as clinically unsuspected pathology and thus imaging has become an integral part of the evaluation of most head and neck cancers patients. This review will focus on the evaluation of cervical adenopathy, summarizing the currently used nomenclature and imaging approach for determining cervical lymph node metastases in head and neck malignancies. The imaging-based classification, which has also been adopted by the American Joint Committee on Cancer, will be presented, the morphologic characteristics used to identify metastatic nodes will be reviewed and the typical nodal spread patterns of the major mucosal cancers of the head and neck will be examined.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
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)0.0000.000

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 head0.067
GPT teacher head0.439
Teacher spread0.372 · 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