A Systematic Review and Meta-Analysis of Diagnostic Performance of Imaging in Acute Cholecystitis
Bibliographic record
Abstract
PURPOSE: To update previously summarized estimates of diagnostic accuracy for acute cholecystitis and to obtain summary estimates for more recently introduced modalities. MATERIALS AND METHODS: A systematic search was performed in MEDLINE, EMBASE, Cochrane Library, and CINAHL databases up to March 2011 to identify studies about evaluation of imaging modalities in patients who were suspected of having acute cholecystitis. Inclusion criteria were explicit criteria for a positive test result, surgery and/or follow-up as the reference standard, and sufficient data to construct a 2 × 2 table. Studies about evaluation of predominantly acalculous cholecystitis in intensive care unit patients were excluded. Bivariate random-effects modeling was used to obtain summary estimates of sensitivity and specificity. RESULTS: Fifty-seven studies were included, with evaluation of 5859 patients. Sensitivity of cholescintigraphy (96%; 95% confidence interval [CI]: 94%, 97%) was significantly higher than sensitivity of ultrasonography (US) (81%; 95% CI: 75%, 87%) and magnetic resonance (MR) imaging (85%; 95% CI: 66%, 95%). There were no significant differences in specificity among cholescintigraphy (90%; 95% CI: 86%, 93%), US (83%; 95% CI: 74%, 89%) and MR imaging (81%; 95% CI: 69%, 90%). Only one study about evaluation of computed tomography (CT) met the inclusion criteria; the reported sensitivity was 94% (95% CI: 73%, 99%) at a specificity of 59% (95% CI: 42%, 74%). CONCLUSION: Cholescintigraphy has the highest diagnostic accuracy of all imaging modalities in detection of acute cholecystitis. The diagnostic accuracy of US has a substantial margin of error, comparable to that of MR imaging, while CT is still underevaluated.
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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 | 0.031 | 0.108 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".