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Record W2750427219 · doi:10.1016/j.nic.2017.03.003

Dual-Energy Computed Tomography

2017· review· en· W2750427219 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

VenueNeuroimaging Clinics of North America · 2017
Typereview
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsJewish General HospitalMcGill University
Fundersnot available
KeywordsDigital Enhanced Cordless TelecommunicationsMedicineNeuroradiologyMedical physicsComputed tomographyWorkflowClinical PracticeHead and neckImage qualityRadiologyComputer visionComputer scienceImage (mathematics)SurgeryNeurology

Abstract

fetched live from OpenAlex

There are increasing applications and use of spectral computed tomography or dual-energy computed tomography (DECT) in neuroradiology and head and neck imaging in routine clinical practice. Part 1 of this 2-part review covered fundamental physical principles underlying DECT scanning and the different approaches for scanning. Part 2 focuses on important and practical considerations for implementing and using DECT in clinical practice, including a review of different images and reconstructions produced by these scanners and important and practical issues, ranging from image quality and radiation dose to workflow-related aspects of DECT scanning, that routinely come up during operationalization of DECT.

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 score0.993
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.334
Teacher spread0.283 · 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