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Record W2146006128 · doi:10.2967/jnmt.111.098632

Small-Animal PET: What Is It, and Why Do We Need It?

2012· review· en· W2146006128 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

VenueJournal of Nuclear Medicine Technology · 2012
Typereview
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComputer sciencePet imagingMedical physicsPositron emission tomographyMedicineNuclear medicine

Abstract

fetched live from OpenAlex

Small-animal PET refers to imaging of animals such as rats and mice using dedicated PET scanners. Small-animal PET has been used extensively in modern biomedical research. It provides a quantitative measure of the 3-dimensional distribution of a radiopharmaceutical administered to a live subject noninvasively. In this article, we will discuss the operational and technical aspects of small-animal PET; make some comparisons between small-animal PET and human PET systems; identify the challenges of, opportunities for, and ultimate limitations in applying small-animal PET; and discuss some representative small-animal PET applications. Education objectives: After reading this article, the technologist will be able to explain the requirements and benefits of small-animal PET in biomedical research, describe the design and general characteristics of a small-animal PET system, list and describe some of the challenges of imaging small animals, and discuss several small-animal PET applications.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.382
Teacher spread0.287 · 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