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Record W3116382769 · doi:10.2967/jnmt.120.261890

Guidelines on Setting Up Stations for Remote Viewing of Nuclear Medicine and Molecular Imaging Studies During COVID-19

2020· article· en· W3116382769 on OpenAlexaff
Jerold W. Wallis, Ran Klein, Tyler Bradshaw, Ciprian Catana, Mathieu Hatt, Richard Laforest, Chi Liu, Osama Mawlawi, K McCall, Dustin Osborne, Jing Tang, R. Glenn Wells, Munir Ghesani

Bibliographic record

VenueJournal of Nuclear Medicine Technology · 2020
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Nuclear medicine imaging2019-20 coronavirus outbreakPandemicSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Medical physicistMedicineMedical imagingProcess (computing)Medical physicsNuclear medicineComputer scienceRadiologyPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The current pandemic has created a situation where nuclear medicine practitioners and medical physicists read or process nuclear medicine images remotely from their home office. This article presents recommendations on the components and specifications when setting up a remote viewing station for nuclear medicine imaging.

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)0.012
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0040.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0210.019

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 head0.070
GPT teacher head0.415
Teacher spread0.345 · 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
GenreMethods

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

Citations6
Published2020
Admission routes1
Has abstractyes

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