Emphasizing the Diagnostic Value of Digital Tomosynthesis in Detecting Hip Fractures
Bibliographic record
Abstract
Our institution recently implemented the use of digital tomosynthesis (DTS) to workup emergency room patients with suspected hip fractures after initial negative or indeterminate radiographs. Our purpose is to evaluate the diagnostic accuracy of DTS for hip fracture detection. We performed a retrospective review of all DTS studies over a 17-month period (July 2017 to November 2018). The results of the radiographs and DTS were recorded as either positive or negative for fracture based on the radiology report. Our reference standard for a fracture was either confirmation on subsequent CT or MRI from the same visit or documentation of clinical findings supportive of a fracture in the patient's electronic medical record. For patients with negative DTS who did not undergo subsequent cross-sectional imaging, a missed fracture was excluded if they did not return within 30 days with a confirmed fracture. Among 91 patients, there were 34 confirmed fractures-sites including, 7 femoral necks, 10 pubic rami, and 7 greater trochanters. DTS was positive for fracture in 29 patients; 28 of these fractures were true positives, 6 confirmed on cross-sectional imaging, and 22 confirmed clinically. One false positive was observed in a patient with no clinical evidence of a fracture. Six fractures were not detected by tomosynthesis but confirmed on CT/MRI. The sensitivity and specificity of DTS are 82% and 98%, respectively, compared to that of radiographs alone at 47% and 96%, respectively. DTS is a promising adjunct to radiographs for hip fracture detection in an emergency department.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".