Detection of Bone Marrow Edema in Nondisplaced Hip Fractures: Utility of a Virtual Noncalcium Dual-Energy CT Application
Bibliographic record
Abstract
Purpose To quantify the sensitivity and specificity of dual-energy computed tomographic (CT) virtual noncalcium images in the detection of nondisplaced hip fractures and to assess whether obtaining these images as a complement to bone reconstructions alters sensitivity, specificity, or diagnostic confidence. Materials and Methods The clinical research ethics board approved chart review, and the requirement to obtain informed consent was waived. The authors retrospectively identified 118 patients who presented to a level 1 trauma center emergency department and who underwent dual-energy CT for suspicion of a nondisplaced traumatic hip fracture. Clinical follow-up was the standard of reference. Three radiologists interpreted virtual noncalcium images for traumatic bone marrow edema. Bone reconstructions for the same cases were interpreted alone and then with virtual noncalcium images. Diagnostic confidence was rated on a scale of 1 to 10. McNemar, Fleiss κ, and Wilcoxon signed-rank tests were used for statistical analysis. Results Twenty-two patients had nondisplaced hip fractures and 96 did not have hip fractures. Sensitivity with virtual noncalcium images was 77% and 91% (17 and 20 of 22 patients), and specificity was 92%–99% (89–95 of 96 patients). Sensitivity increased by 4%–5% over that with bone reconstruction images alone for two of the three readers when both bone reconstruction and virtual noncalcium images were used. Specificity remained unchanged (99% and 100%). Diagnostic confidence in the exclusion of fracture was improved with combined bone reconstruction and virtual noncalcium images (median score: 10, 9, and 10 for readers 1, 2, and 3, respectively) compared with bone reconstruction images alone (median score: 9, 8, and 9). Conclusion When used as a supplement to standard bone reconstructions, dual-energy CT virtual noncalcium images increased sensitivity for the detection of nondisplaced traumatic hip fractures and improved diagnostic confidence in the exclusion of these fractures. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 17, 2017.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".