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Record W2082614986 · doi:10.1039/c3cc41554f

Tumour targeting with radiometals for diagnosis and therapy

2013· review· en· W2082614986 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.

Bibliographic record

VenueChemical Communications · 2013
Typereview
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of British ColumbiaTRIUMF
Fundersnot available
KeywordsNuclidePositron emission tomographyMedical physicsNuclear medicineSingle-photon emission computed tomographyEmission computed tomographyComputer scienceMedicinePhysicsNuclear physics

Abstract

fetched live from OpenAlex

Use of radiometals in nuclear oncology is a rapidly growing field and encompasses a broad spectrum of radiotracers for imaging via PET (positron emission tomography) or SPECT (single-photon emission computed tomography) and therapy via α, β(-), or Auger electron emission. This feature article opens with a brief introduction to the imaging and therapy modalities exploited in nuclear medicine, followed by a discussion of the multi-component strategy used in radiopharmaceutical development, known as the bifunctional chelate (BFC) method. The modular assembly is dissected into its individual components and each is discussed separately. The concepts and knowledge unique to metal-based designs are outlined, giving insight into how these radiopharmaceuticals are evaluated for use in vivo. Imaging nuclides (64)Cu, (68)Ga, (86)Y, (89)Zr, and (111)In, and therapeutic nuclides (90)Y, (177)Lu, (225)Ac, (213)Bi, (188)Re, and (212)Pb will be the focus herein. Finally, key examples have been extracted from the literature to give the reader a sense of breadth of the field.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.215
GPT teacher head0.440
Teacher spread0.225 · 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