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Record W2899551038 · doi:10.2967/jnmt.118.218131

Technologist Approach to Global Dose Optimization

2018· article· en· W2899551038 on OpenAlex
Pedro Fragoso Costa, Giorgio Testanera, Luca Camoni, Christelle Terwinghe, Elizabeth Bailey, Norman E. Bolus, Tina Alden

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 · 2018
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsNuclear medicineMedical physicsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Nuclear medicine technologists are specialized health professionals who cover a wide range of tasks from clinical routine (including image acquisition and processing, radiopharmaceutical dispensing and administration, patient care, and radioprotection tasks) to leading clinical research in the field of nuclear medicine. As a fundamental concern in all radiation sciences applied to medicine, protection of individuals against the harmful effects of ionizing radiation must be constantly revised and applied by the professionals involved in medical exposures. The acknowledgment that nuclear medicine technologists play a prominent role in patient management and several procedural steps, both in diagnostic and in therapeutic nuclear medicine applications, carries the duty to be trained and knowledgeable on the topic of radiation protection and dose optimization. An overview on selected topics related to dose optimization is presented in this article, reflecting the similarities and particularities of dose reduction-related principles, initiatives, and practicalities from a global perspective.

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)0.001
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.025
GPT teacher head0.339
Teacher spread0.313 · 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