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Record W3115246370 · doi:10.3390/pharmaceutics13010049

Targeted Alpha Therapy: Progress in Radionuclide Production, Radiochemistry, and Applications

2020· review· en· W3115246370 on OpenAlexaff
Bryce J. B. Nelson, Jan Andersson, Frank Wuest

Bibliographic record

VenuePharmaceutics · 2020
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsParticle therapyAlpha particleMedical physicsRadionuclide therapyMedicineRadiation therapyNuclear medicineNuclear physicsPhysicsInternal medicine

Abstract

fetched live from OpenAlex

This review outlines the accomplishments and potential developments of targeted alpha (α) particle therapy (TAT). It discusses the therapeutic advantages of the short and highly ionizing path of α-particle emissions; the ability of TAT to complement and provide superior efficacy over existing forms of radiotherapy; the physical decay properties and radiochemistry of common α-emitters, including 225Ac, 213Bi, 224Ra, 212Pb, 227Th, 223Ra, 211At, and 149Tb; the production techniques and proper handling of α-emitters in a radiopharmacy; recent preclinical developments; ongoing and completed clinical trials; and an outlook on the future of TAT.

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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.100
GPT teacher head0.432
Teacher spread0.332 · 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
GenreReview

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

Citations179
Published2020
Admission routes1
Has abstractyes

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