Traumatic Ankle Injuries in the Emergency Department: Evaluating the Adequacy of Clinical Documentation With Reference to the Ottawa Ankle Rules
Bibliographic record
Abstract
Introduction Ankle trauma is a frequent presentation to the emergency department (ED) and poses a significant demand on radiology services. Efficient triage is essential to identify patients requiring urgent imaging, optimising resources, reducing waiting times, and minimising unnecessary radiation exposure. Injuries range from soft tissue damage to fractures of the distal tibia, fibula, or malleoli. Not all ankle injuries require immediate imaging, and clinical decision-making tools can guide the use of radiographs. The Ottawa Ankle Rules (OAR) are a validated tool, endorsed by the Royal College of Radiologists (RCR), designed to identify ankle injuries requiring radiographs. Imaging is indicated if the patient is unable to bear weight both immediately after injury and during examination, or if there is bone tenderness along the distal six centimetres of the posterior edge of the tibia or fibula, or over the lateral or medial malleoli. Adherence to the OAR has been shown to reduce unnecessary imaging while maintaining high sensitivity for fractures. Methods A two-cycle retrospective review was conducted of patients presenting with traumatic ankle injuries who underwent radiography. Records were reviewed via the Picture Archiving and Communication System (PACS) until 100 patients per cycle were included. Data collected included whether the OAR were referenced in radiograph requests and whether a fracture was diagnosed. After the first cycle, educational interventions were implemented to increase guideline awareness. The second cycle evaluated the impact of these interventions. Statistical analysis assessed the significance of observed changes. Exclusions included patients aged ≤16 years or ≥55 years, those with non-traumatic or chronic ankle presentations, and those with polytrauma or high-energy mechanisms of injury. Results were compared against RCR standards, which state that 100% of all ankle plain-film requests for trauma should reference the OAR. Results were presented at departmental governance meetings, and recommendations were subsequently implemented. Results In the first cycle, 37 (37%) of radiograph requests referenced the OAR. Fractures were identified in 21 (21%) patients, with 13 (61.9%) of these requests documenting the OAR and 8 (38.1%) omitting them. Following educational interventions, the second cycle demonstrated significant improvement, with 51 (51%) of requests referencing the OAR (P = 0.046). The proportion of patients with fractures increased to 31 (31%), of whom 26 (83.9%) had OAR documented, compared with 5 (16.1%) without documentation (P < 0.001). These findings reinforce that adherence to the OAR improves fracture detection, reduces unnecessary imaging, and remains a sensitive tool for ankle trauma assessment. Conclusion This quality improvement project demonstrates that focused interventions, particularly clinician education and guideline awareness, can enhance adherence to the OAR. Increased documentation and use of the OAR were associated with a higher diagnostic yield for fractures, improved triage, and a potential reduction in unnecessary imaging. Although full compliance was not achieved, incremental improvements contribute to patient safety and alignment with national recommendations. Future strategies may include regular departmental teaching sessions and system-based interventions such as electronic prompts to sustain compliance.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".