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Record W4387965395 · doi:10.1186/s41181-023-00218-y

Highlight selection of radiochemistry and radiopharmacy developments by editorial board

2023· review· en· W4387965395 on OpenAlexaff
Jean N. DaSilva, Clemens Decristoforo, Robert H. Mach, Guy Bormans, Giuseppe Carlucci, Mohammed Al‐Qahtani, Adriano Duatti, Antony D. Gee, Wiktor Szymański, Sietske Rubow, Jeroen J. M. A. Hendrikx, Xing Yang, Hongmei Jia, Junbo Zhang, Peter Caravan, Hua Yang, Jan Rijn Zeevaart, Miguel Avila Rodriquez, Ralph Santos Oliveira, Marcela Zubillaga, Tamer M. Sakr, Sarah Spreckelmeyer

Bibliographic record

VenueEJNMMI Radiopharmacy and Chemistry · 2023
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsTRIUMFUniversité de Montréal
FundersCharité – Universitätsmedizin Berlin
KeywordsEditorial boardScope (computer science)Selection (genetic algorithm)Audience measurementVariety (cybernetics)Investigational DrugsEngineering ethicsLibrary scienceComputer scienceEngineeringPolitical scienceBiologyBioinformaticsClinical trial

Abstract

fetched live from OpenAlex

BACKGROUND: The Editorial Board of EJNMMI Radiopharmacy and Chemistry releases a biannual highlight commentary to update the readership on trends in the field of radiopharmaceutical development. MAIN BODY: This selection of highlights provides commentary on 21 different topics selected by each coauthoring Editorial Board member addressing a variety of aspects ranging from novel radiochemistry to first-in-human application of novel radiopharmaceuticals. CONCLUSION: Trends in radiochemistry and radiopharmacy are highlighted. Hot topics cover the entire scope of EJNMMI Radiopharmacy and Chemistry, demonstrating the progress in the research field in many aspects.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.997
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0450.025

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 head0.036
GPT teacher head0.371
Teacher spread0.335 · 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.

Study designNot applicable
DomainEvaluation
GenreEditorial

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

Citations1
Published2023
Admission routes1
Has abstractyes

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