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Record W1994806257 · doi:10.1016/j.carj.2014.12.005

Canadian Association of Radiologists Radiation Protection Working Group: Automated Patient-Specific Dose Registries—What Are They and What Are They Good for?

2015· review· en· W1994806257 on OpenAlexaffabout
Thorarin A. Bjarnason, Yogesh Thakur, Santanu Chakraborty, Peter Liu, Martin O’Malley, Richard Coulden, Michelle Noga, Andrew C. Mason, John R. Mayo

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2015
Typereview
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsEagle Ridge HospitalUniversity of AlbertaUniversity of TorontoCARE CanadaUniversity of OttawaVancouver Coastal HealthUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaPrincess Margaret Cancer CentreOttawa HospitalInterior Health
Fundersnot available
KeywordsMedicineRadiation doseMedical physicsRadiation protectionPatient careEffective dose (radiation)Radiation exposureHealth careMedical emergencyNuclear medicineNursing

Abstract

fetched live from OpenAlex

Medical radiation should be used appropriately and with a dose as low as reasonably achievable. Dose monitoring technologies have been developed that automatically accumulate patient dose indicators, providing effective dose estimates and patient-specific dose histories. Deleterious radiation related events have prompted increased public interest in the safe use of medical radiation. Some view individualized patient dose histories as a tool to help manage the patient dose. However, it is imperative that dose monitoring technologies be evaluated on the outcomes of dose reduction and effective patient management. Patient dose management needs to be consistent with the widely accepted linear no-threshold model of stochastic radiation effects. This essay reviews the attributes and limitations of dose monitoring technologies to provoke discussion regarding resource allocation in the current fiscally constrained health care system.

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 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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.954
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.001
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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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 teacher head, not a consensus.

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

Citations5
Published2015
Admission routes2
Has abstractyes

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