CT in Adults: Systematic Review and Meta-Analysis of Interpretation Discrepancy Rates
Bibliographic record
Abstract
PURPOSE: To use meta-analysis to determine the discrepancy rate when interpreting computed tomography (CT) studies performed in adult patients and to determine whether discrepancy rate differs on the basis of body region or level of radiologist training. MATERIALS AND METHODS: MEDLINE and EMBASE were searched from 1946 to June 2012 by using the combination "radiology AND (error OR peer review)." Two reviewers independently selected studies that met the inclusion criteria and extracted study data. Total and major discrepancy rates were investigated with a random-effects meta-analysis, and subgroups were compared by using the χ(2) Q statistic. Subgroup analyses were performed on the basis of the level of training of the initial radiologist and the body system scanned. RESULTS: Fifty-eight studies met the inclusion criteria (388 123 CT examinations). The pooled total discrepancy rate was 7.7% (95% confidence interval [CI]: 5.6%, 10.3%), and the major discrepancy rate was 2.4% (95% CI: 1.7%, 3.2%). The pooled major discrepancy rate was comparable for staff (2.9%; 95% CI: 1.2%, 6.7%) and residents (2.2%; 95% CI: 1.7%, 2.9%) (Q = 0.92, P = .633). The pooled major discrepancy rates for head CT (0.8%; 95% CI: 0.4%, 1.6%) and spine CT (0.7%; 95% CI: 0.2%, 2.7%) were lower than those for chest CT (2.8%; 95% CI: 1.5%, 5.4%) and abdominal CT (2.6%; 95% CI: 1.0%, 6.7%) (Q = 8.28, P = .041). Lack of blinding of the reference radiologist to the initial report was associated with a lower major discrepancy rate (2.0%; 95% CI: 1.4%, 2.7%; 43 studies) than when blinding was present (12.1%; 95% CI: 4.4%, 29.4%; five studies) (Q = 10.65, P = .001). CONCLUSION: Potentially useful reference ranges were identified in the subgroup analyses on the basis of body region scanned at adult CT. However, considerable heterogeneity that is only partially explained by subgroup analysis signifies that further research is necessary--particularly regarding the question of blinding of the reference radiologist.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".