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Record W3012169121 · doi:10.1177/0846537120906482

Emergency Imaging in Pregnancy and Lactation

2020· review· en· W3012169121 on OpenAlexaff
Shobhit Mathur, Ravishankar Pillenahalli Maheshwarappa, Saman Fouladirad, Omar Metwally, Pratik Mukherjee, Amy Lin, Aditya Bharatha, Savvas Nicolaou, Noah Ditkofsky

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2020
Typereview
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMagnetic resonance imagingMedical imagingPregnancyMedical physicsRadiology

Abstract

fetched live from OpenAlex

The use of diagnostic imaging studies in the emergency setting has increased dramatically over the past couple of decades. The emergency imaging of pregnant and lactating patients poses unique challenges and calls upon the crucial role of radiologists as consultants to the referring physician to guide appropriate use of imaging tests, minimize risk, ensure timely management, and occasionally alleviate unwarranted trepidation. A clear understanding of the risks and benefits involved with various imaging tests in this patient population is vital to achieve this. This review discusses the different safety and appropriateness issues that could arise with the use of ionizing radiation, iodinated-, and gadolinium-based contrast media and radiopharmaceuticals in pregnant and lactating patients. Special considerations such as trauma imaging, safety concerns with magnetic resonance imaging and ultrasound, management of claustrophobia, contrast extravasation, and allergic reactions are also reviewed. The consent process for these examinations has also been described.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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 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

Citations25
Published2020
Admission routes1
Has abstractyes

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