The Sonographic Ottawa Foot and Ankle Rules Study (the SOFAR Study)
Bibliographic record
Abstract
INTRODUCTION: Foot and ankle injuries are common in the Emergency Department (ED). Of those requiring radiographs in accordance with the Ottawa Foot and Ankle Rules, approximately 22% have a fracture. Ultrasound is developing as a tool for emergency musculoskeletal assessment--it is inexpensive and rapid, and visualises soft tissue and bony structures. METHODS: This diagnostic cohort study examined if ultrasound could detect acute bony foot and ankle injuries. Ottawa Rules-positive patients over 16 years were eligible. An ultrasound scan (USS) was performed blind to radiograph findings by an ED member. Patient management was according to radiograph. Significant fractures were defined as per the Ottawa Foot and Ankle Rules study group. All radiographic reporting was conducted blind to USS findings. All USS operators received specific 2-day training in musculoskeletal ultrasound. RESULTS: 110 subjects were recruited. 11 had significant radiological fractures, and 10 were seen on ultrasound. The single missed fracture arose due to the operator not scanning proximally enough on the fibula. On rescanning following radiograph review, the fracture was clearly seen. The sensitivity of USS is 90.9% (95% CI 65.7 to 98.3), and the specificity is 90.9% (95% CI 88.1 to 91.7). The positive predictive value is 0.526 (95% CI 0.380 to 0.569). The negative predictive value is 0.989 (95% CI 0.959 to 0.998). The positive likelihood ratio is 10.00 (95% CI 5.526 to 11.901), and the negative likelihood ratio is 0.100 (95% CI 0.018 to 0.389). CONCLUSION: This pilot study demonstrates that ultrasound shows great promise for the sensitive detection of foot and ankle fractures, thus identifying patients who require radiographic evaluation more efficiently.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".