LOW RISK RULE: HIGH QI REWARD – APPLICATION OF THE LOW RISK ANKLE RULE (LRAR) IN THE SICKKIDS EMERGENCY DEPARTMENT
Bibliographic record
Abstract
BACKGROUND: At baseline an audit demonstrated the rate of ankle radiography for acute ankle injuries in the ED at approximately 90%. An evidence-based decision rule, The Low Risk Ankle Rule (LRAR) by Boutis et al., has been developed and validated for the purposes of determining which ankle injuries do not necessitate radiography. The rule has 100% sensitivity for capturing clinically important ankle injuries and in the literature has reduced ankle x-ray imaging by 30% when applied. OBJECTIVES: Our aim was to achieve a 30% reduction in ankle x-rays for acute ankle injuries ordered by June 30th, 2016. Measures of interest included: Outcome: • X-rays ordered for ankle injuries • ED Length of Stay (PIA to discharge) Process: • Documentation of LRAR in medical record • Use of developed ankle x-ray form Balancing: • Return to ED visits within 72H for same reason • Orthopaedic clinic referrals DESIGN/METHODS: Project implementation was approved by the local quality improvement board. The project was prioritized as part of an institutional Choosing Wisely campaign initiative. Relevant patient data was obtained by reviewing the electronic medical records of patients with acute ankle injuries. Major improvement initiatives included: 1) Staff (medical, nursing) education surrounding the LRAR, 2) LRAR reference posters within the ED, and 3) Development of a new mandatory diagnostic requisition for ankle injuries in collaboration with the Division of Radiology that encourages use of the LRAR. RESULTS: Ankle X-ray ordering rates decreased significantly following implementation of changes (~90% to ~56%) and ED length of stay decreased in patients with ankle injuries. Increased reference in documentation to the LRAR and use of the developed radiology requisition form was seen. Return visits to the ED and Orthopedic clinic referrals did not increase. Measures taken six months form the time of initial quality improvement efforts supported sustained quality improvement in this area. CONCLUSION: To date, the results have not been replicated outside of the original setting. We intend to further sustain gains by having implemented a hard-stop radiology requisition for all ankle injuries, creating a new electronic documentation template for ankle injuries, continuing regular monitoring of x-ray rates, and handing over of key project roles to local champions for long-term maintenance.
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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 | 0.015 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".