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Record W4416998611 · doi:10.2967/jnumed.125.270478

Summary of the 2024 Update of the North American Guidelines for Pediatric Administered Radiopharmaceutical Activities

2025· article· en· W4416998611 on OpenAlexaff
S. Ted Treves, Frederic H. Fahey, Valentina Ferrer Valencia, Nanci A. Burchell, Christiane Sarah Burton, Michael Czachowski, Frederick D. Grant, Hollie Lai, Ruth Lim, Helen Nadel, Miguel Hernandez Pampaloni, Neeta Pandit‐Taskar, Marguerite T. Parisi, Victor J. Seghers, Summit Shah, Barry L. Shulkin, Lisa J. States, Reza Vali, Don C. Yoo, Katherine Zukotynski

Bibliographic record

VenueJournal of Nuclear Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsMcMaster UniversityHospital for Sick Children
FundersSociety of Nuclear Medicine and Molecular Imaging
KeywordsMEDLINEGuidelineRisk assessmentSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Abstract

fetched live from OpenAlex

The North American consensus guidelines for pediatric administered radiopharmaceutical activities (NAGL) were first published in 2011 and subsequently updated in 2014 and 2016 ([1][1]–[3][2]). The NAGL have been cited in many scientific publications, reviews, and procedure guidelines issued by the

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)0.008
metaresearch head score (Gemma)0.018
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.015

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 head0.108
GPT teacher head0.436
Teacher spread0.328 · 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
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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