Reducing Unnecessary X-Rays for Nasal Fractures: A Quality Improvement Project
Bibliographic record
Abstract
Background Choosing Wisely Canada recommends against the use of nasal bone X-rays for the evaluation of nasal fractures. The goal of this quality improvement project was to reduce the number of nasal bone X-rays ordered at our institution by 50% by 1 year. Methods The Institute for Healthcare Improvement Model for Improvement was used, and a pre- and post-intervention study was conducted. Change ideas included the following: a clinical decision support tool, provider surveys, and education. The number of X-rays ordered monthly was monitored. Financial cost (labor, materials, and overhead) was assessed. Environmental impact was extrapolated based on carbon dioxide equivalent emissions (CO 2 e). Balancing measures included the use of computed tomography (CT) scans. Analysis included summary statistics, statistical process control charting, and unpaired t-tests. Results There was a 73% reduction in total X-rays ordered from 197 pre-intervention (September 2021-November 2022) to 58 post-intervention (December 2022-February 2024). There was a statistically-significant decrease in difference of means of 2.6 X-rays/month (4.9 vs 2.3, pre vs post; P < .001), an average monthly reduction of 53%. There was special cause variation after implementation. Cost savings was $5534.98, and environmental footprint reduction was 111.2 kg of CO 2 e. There was no compensatory increase in the number of CT scans ordered. Conclusion Implementation of a clinical decision support tool and education resulted in a significant reduction in the number of nasal bone X-rays ordered for the evaluation of nasal fractures. This Choosing Wisely Canada project ultimately reduces unnecessary investigations for patients, saves health care costs, and reduces environmental impact.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".