MétaCan
Menu
Back to cohort
Record W2226211568 · doi:10.1586/14737140.2015.1108193

Advanced dual-energy CT for head and neck cancer imaging

2015· review· en· W2226211568 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

VenueExpert Review of Anticancer Therapy · 2015
Typereview
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineDigital Enhanced Cordless TelecommunicationsHead and neck cancerHead and neckRadiologyComputed tomographyCancerHead and neck squamous-cell carcinomaRadiation therapyMedical physicsNuclear medicineSurgeryComputer science

Abstract

fetched live from OpenAlex

Dual energy computed tomography (DECT) is an advanced form of computed tomography (CT) in which simultaneous or near-simultaneous acquisitions are performed at two different peak energy levels, enabling material density and spectral attenuation characterization beyond what is possible with conventional CT scans. This article is a review of the current applications of DECT for the evaluation of head and neck cancer. The article will begin with a brief overview of different approaches to DECT scanning and address basic issues related to image quality and acquisition dose. This will be followed by a review of the use of different DECT reconstructions for improving head and neck squamous cell carcinoma visualization, evaluation of tumor extent, and invasion of critical structures. The article will conclude with a brief review of other emerging applications of DECT for evaluation of different head and neck cancers and advanced tumor analysis.

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 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 score0.951
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
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 head0.035
GPT teacher head0.387
Teacher spread0.353 · 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