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Record W4386890226 · doi:10.51731/cjht.2023.739

Optimizing the Use of Iodinated Contrast Media: Conservation Strategies Used Across Canada During the 2022 Shortage

2023· article· en· W4386890226 on OpenAlexaboutno aff
CADTH

Bibliographic record

VenueCanadian Journal of Health Technologies · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic shortageContrast (vision)DosingMedicineIodinated contrast mediaIodinated contrastAdverse effectSustainabilityRadiologyComputed tomographyComputer sciencePharmacology

Abstract

fetched live from OpenAlex


 A shortage of iodinated contrast media (ICM) used in contrast-enhanced CT exams led to the adoption of necessary conservation strategies across Canada.
 Conservation strategies included multidispensing from single-use and multiuse ICM bottles, diluting or reducing ICM dose volumes, switching to weight-based dosing from fixed-based dosing, lower tube voltage, performing unenhanced CT scans, using alternative imaging modalities, or prioritizing urgent cases. One of the more common alternative conservation strategies was to prioritize urgent cases for contrast-enhanced CT exams.
 Most medical imaging staff who responded to a national survey on ICM conservation strategies reported they would return to their regular doses used before the shortage despite little to no perceived effect on the contrast conspicuity of images or on patient adverse events with reduced ICM volumes.
 The ICM shortage represents an opportunity to reconsider ICM usage practices given environmental sustainability concerns with ICM and potential cost savings in reducing its use.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.251
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes1
Has abstractyes

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