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Automated radiosynthesis of 68Ga for large-scale routine production using 68Zn pressed target

2019· article· en· W2992727319 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueApplied Radiation and Isotopes · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversité de Sherbrooke
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaUniversité de SherbrookeCancer Research Society
KeywordsRadiosynthesisRadiochemistryPositron emission tomographyPet imagingChemistryMaterials scienceNuclear medicineMedicine

Abstract

fetched live from OpenAlex

Gallium-68 (68Ga) has attracted increasing interest in recent years due to the expanding clinical applications of 68Ga-based radiopharmaceuticals (Rahbar et al., 2017). 68Ga is mainly produced via 68Ge/68Ga generators that are limited in yield by the 68Ge activity (typically up to 1.85 GBq at calibration time). With the increased-demand of 68Ga in nuclear medicine for positron emission tomography (PET) imaging, there is a need for more efficient and robust production methods to obtain larger amounts of [68Ga]GaCl3 with high radionuclidic and radiochemical purity and apparent molar activity (AMA) for facilitating the distribution of 68Ga-based radiopharmaceuticals. The objectives of this study were to develop a fast and efficient process for the preparation of 68Zn-based solid targets and to optimize the critical parameters for the automated radiosynthesis of [68Ga]GaCl3 for large-scale routine production.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.294
Teacher spread0.279 · 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