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Record W2945870813 · doi:10.1515/ract-2019-0005

Radiochemical aspects of alpha emitting radionuclides for medical application

2019· article· en· W2945870813 on OpenAlexaff
Maryline G. Ferrier, Valery Radchenko, D. Scott Wilbur

Bibliographic record

VenueRadiochimica Acta · 2019
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsChemistryRadionuclideRadiochemistryAlpha particleNuclear physicsPhysics

Abstract

fetched live from OpenAlex

Abstract The use of α-emitting radionuclides in targeted alpha therapy (TAT) holds great potential for treatment of human diseases, such as cancer, due to the short pathlength and high potency of the α particle, which can localize damage to targeted cells while minimizing effects to healthy surrounding tissues. In this review several potential α-emitting radionuclides having emission properties applicable to TAT are discussed from a radiochemical point of view. Overviews of production, radiochemical separation and chelation aspects relative to developing TAT radiopharmaceuticals are provided for the α-emitting radionuclides (and their generator systems) 211At, 224Ra/212Pb/212Bi, 225Ac/213Bi, 227Th/223Ra, 230U/226Th, 149Tb and 255Fm.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.311
Teacher spread0.300 · 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
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

Citations57
Published2019
Admission routes1
Has abstractyes

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