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Record W2530986656 · doi:10.1016/j.carj.2016.05.006

Adverse Reactions to Contrast Material: A Canadian Update

2016· review· en· W2530986656 on OpenAlexaffabout
Alexander Morzycki, Anuj Bhatia, Kieran J. Murphy

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2016
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity Health NetworkDalhousie University
Fundersnot available
KeywordsMedicineAdverse effectIntensive care medicineAnaphylaxisIodinated contrastAnaphylactoid reactionsRadiologyInternal medicineAllergyComputed tomography

Abstract

fetched live from OpenAlex

Imaging techniques frequently employ contrast agents to improve image resolution and enhance pathology detection. These gadolinium- and iodine-based media, although generally considered safe, are associated with a number of adverse effects ranging from mild to severe. Reactions are classified as either anaphylactoid ("anaphylaxis-like") or nonanaphylactoid, depending on a number of elements that will be reviewed. Herein, we have summarized predisposing risk factors for adverse events resulting from the use of contrast, their associated pathophysiological mechanisms as well as known prophylaxis for the antitreatment of high-risk patients. In the unlikely event that a serious adverse reaction does occur, we have provided a comprehensive summary of treatment protocols. Our goal was to thoroughly evaluate the current literature regarding adverse reactions to radiocontrast agents and provide an up to date review for the health care provider.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.951
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.248
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations53
Published2016
Admission routes2
Has abstractyes

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