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Record W2736585478 · doi:10.1016/j.carj.2017.02.004

Utility of Magnetic Resonance Imaging for the Diagnosis of Appendicitis during Pregnancy: A Canadian Experience

2017· article· en· W2736585478 on OpenAlexaffabout
Michael Burns, Cameron Hague, Patrick M. Vos, Pari Tiwari, Sam M. Wiseman

Bibliographic record

VenueCanadian Association of Radiologists Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineMagnetic resonance imagingAppendicitisRadiologyPregnancyAppendixRetrospective cohort studyGeneral surgerySurgery

Abstract

fetched live from OpenAlex

PURPOSE: The objective of the study was to evaluate the performance of magnetic resonance imaging (MRI) for the diagnosis of appendicitis during pregnancy. METHODS: We conducted a retrospective review of all MRI scans performed at our institution, between 2006 and 2012, for the evaluation of suspected appendicitis in pregnant women. Details of the MRI scans performed were obtained from the radiology information system as well as details of any ultrasounds carried out for the same indication. Clinical and pathological data were obtained by retrospective chart review. RESULTS: The study population comprised 63 patients, and 8 patients underwent a second MRI scan during the same pregnancy. A total of 71 MRI scans were reviewed. The appendix was identified on 40 scans (56.3%). Sensitivity of MRI was 75% and specificity was 100% for the diagnosis of appendicitis in pregnant women. When cases with right lower quadrant inflammatory fat stranding or focal fluid, without appendix visualization, were classified as positive for appendicitis, MRI sensitivity increased to 81.3% but specificity decreased to 96.4%. CONCLUSIONS: MRI is sensitive and highly specific for the diagnosis of appendicitis during pregnancy and should be considered as a first line imaging study for this clinical presentation.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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 designObservational
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

Citations28
Published2017
Admission routes2
Has abstractyes

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