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Record W4387141463 · doi:10.2967/jnumed.123.265906

The Development and Validation of Radiopharmaceuticals Targeting Bacterial Infection

2023· article· en· W4387141463 on OpenAlexaff
Alberto Signore, Alvaro A. Ordoñez, C. Arjun, Gurpreet Kaur Aulakh, Nicolas Bézière, Ekaterina Dadachova, Thomas Ebenhan, Ulises Granados, Aruna Korde, Amir Reza Jalilian, Wening Lestari, Archana Mukherjee, Miloš Petřík, Tamer M. Sakr, Clara Santos‐Cuevas, Mick M. Welling, Jan Rijn Zeevaart, Sanjay K. Jain, David M. Wilson

Bibliographic record

VenueJournal of Nuclear Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute of Allergy and Infectious Diseases
KeywordsAtomic energyAgency (philosophy)Medical physicsMedicineClinical PracticeEngineering ethicsFamily medicineEngineeringSociology

Abstract

fetched live from OpenAlex

The International Atomic Energy Agency organized a technical meeting at its headquarters in Vienna, Austria, in 2022 that included 17 experts representing 12 countries, whose research spanned the development and use of radiolabeled agents for imaging infection. The meeting focused largely on bacterial pathogens. The group discussed and evaluated the advantages and disadvantages of several radiopharmaceuticals, as well as the science driving various imaging approaches. The main objective was to understand why few infection-targeted radiotracers are used in clinical practice despite the urgent need to better characterize bacterial infections. This article summarizes the resulting consensus, at least among the included scientists and countries, on the current status of radiopharmaceutical development for infection imaging. Also included are opinions and recommendations regarding current research standards in this area. This and future International Atomic Energy Agency-sponsored collaborations will advance the goal of providing the medical community with innovative, practical tools for the specific image-based diagnosis of infection.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.771
Threshold uncertainty score0.110

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.051
GPT teacher head0.371
Teacher spread0.320 · 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 teacher head, not a consensus.

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

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

Citations22
Published2023
Admission routes1
Has abstractyes

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