The Accuracy of Bedside Ultrasonography as a Diagnostic Tool for Fractures in the Ankle and Foot
Bibliographic record
Abstract
OBJECTIVES: Ultrasonography (US) has been shown to be helpful in diagnosing fractures in the emergency department (ED) setting. The aim of this study was to determine the diagnostic accuracy of US for fractures in patients presenting to the ED with foot and/or ankle sprain and positive Ottawa foot and ankle rules. METHODS: This was a prospective study of consecutive patients aged 18 years and over were admitted to the ED with acute foot and/or ankle sprain and positive Ottawa foot and ankle rules. After the patients by were examined by bedside US, anteroposterior and lateral ankle radiographs were obtained, as well as anteroposterior and oblique foot radiographs. The films were evaluated by an orthopedic surgeon who was blinded to the US examination results. The orthopedic surgeon's evaluation was considered the criterion standard for diagnosing a fracture. RESULTS: A total of 246 patients were included in the study. In 76 (30.9%) of the patients, a total of 79 fractures were detected by radiography. Ten false-negative and nine false-positive results were obtained by US examination. Only one patient, whose US showed a fracture but whose radiographs were normal, had a fracture detected by computed tomography (CT). The sensitivity and specificity of US scanning in detecting fractures were 87.3% (95% confidence interval [CI] = 77.5% to 93.4%) and 96.4% (95% CI = 93.1% to 98.2%), respectively. CONCLUSIONS: Ultrasound had good sensitivity and specificity for diagnosing fifth metatarsal, lateral, and medial malleolus fractures in the patients with foot and/or ankle sprain. However, sensitivity and specificity of US for navicular fractures were low.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".