Bone islands incidentally detected on computed tomography: frequency of enostosis and differentiation from untreated osteoblastic metastases based on CT attenuation value
Bibliographic record
Abstract
OBJECTIVE: The frequency of enostosis incidentally found on CT and CT attenuation value to distinguish them from untreated osteoblastic metastases (UOM). METHODS: Enostosis group: 46 polytrauma patients underwent thoracoabdominal CT. Inclusion criteria: age range 14-35 years. Exclusion criteria: cancer, previous fractures. UOM group: 20 patients with radiological diagnosis of UOM. Analyzed data: number, size, location and density of enostoses and metastases. The density was measured with the broadest possible region of interest at the center of the lesion by two radiologists independently. Receiver operatingcharacteristic analysis to determine the sensitivity and specificity, area under the curve 95% confidence intervals and cutoff values of CT density to differentiate metastases from enostoses. RESULTS: Patients were 28 ± 7 years old (72% males). 41 (89%) patients had 124 enostoses (2-15 mm) with an average density of 1007 ± 122 Hounsfiled unit (HU, observer1) and 1052 ± 107 (observer2). The most common sites of occurrence were the proximal femur (34%), the pelvis (22%), the acetabulum (20%), the proximal humerus (11%), the vertebrae (11%) and the rib (2%). 13 patients had 1 bone island, 8 patients had 2, 9 cases had 3 and 11 cases had more than 3 enostoses. Overall, 114 UOM were evaluated, their average density was 728 ± 163 HU (observer1) and 712 ± 178 HU (observer2). The area under the curve value of mean density to distinguish enostoses from UOM was 0,982. Using a cut-off of 881 HU for mean density, sensitivity was 98% and specificity 95%. CONCLUSION: The frequency of enostosis in this study is 89%. The average density identified can help to distinguish enostoses from UOM. ADVANCES IN KNOWLEDGE: We report the exact frequency of enostosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".