MétaCan
Menu
Back to cohort
Record W2116098344 · doi:10.2214/ajr.11.7405

Imaging of Pregnant and Lactating Patients: Part 1, Evidence-Based Review and Recommendations

2012· review· en· W2116098344 on OpenAlexaff
Page I. Wang, Suzanne T. Chong, Ania Z. Kielar, Aine Marie Kelly, Ursula D. Knoepp, Michael B. Mazza, Mitchell M. Goodsitt

Bibliographic record

VenueAmerican Journal of Roentgenology · 2012
Typereview
Languageen
FieldMedicine
TopicPregnancy-related medical research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineIodinated contrastPregnancyLactationGadoliniumRadiation exposureUltrasoundRadiologyObstetricsNuclear medicineComputed tomography

Abstract

fetched live from OpenAlex

OBJECTIVE: The objectives of this article are to discuss the current evidence-based recommendations regarding radiation dose concerns, the use of iodinated and gadolinium-based contrast agents, and the comparative advantages of multimodality imaging (ultrasound, CT, and MRI) during pregnancy and lactation. We also discuss the use of imaging to evaluate pregnant trauma patients. CONCLUSION: Maternal and fetal radiation exposure and dose are affected by gestational age, anatomic site, modality, and technique. The use of iodinated and gadolinium-based contrast agents during pregnancy and lactation has not been well studied in human subjects. Imaging should be used to evaluate pregnant trauma patients only when the benefits outweigh the risks.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.006
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.087
GPT teacher head0.405
Teacher spread0.318 · 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 designSystematic review
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

Citations245
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of RoentgenologySame topicPregnancy-related medical researchFrench-language works237,207