Utility of Magnetic Resonance Imaging for the Diagnosis of Appendicitis during Pregnancy: A Canadian Experience
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".