Appropriate use of CT for patients presenting with suspected renal colic: a quality improvement study
Bibliographic record
Abstract
Introduction: CT use for renal colic has increased costs, radiation exposure and frequently does not alter management. Consequently, choosing wisely (CW) recommends avoiding CT imaging of otherwise healthy patients younger than 50 years presenting with symptoms of recurrent, uncomplicated renal colic. We evaluated the utilisation of CT imaging for this subgroup of patients and subsequently implemented a quality improvement initiative with an aim to reduce unnecessary radiation exposure. Methods: A retrospective chart review was performed for all patients younger than 50 years who visited Sunnybrook Health Sciences Centre emergency department (ED) between December 2015 and May 2016 with a discharge diagnosis of renal colic. After the audit period, emergency physicians were engaged to perform a root cause analysis and a driver diagram was developed. In December 2016, a clinical decision tool was introduced to standardise the imaging for patients with presumed renal colic. In May 2017, a separate electronic order was created for low-dose CT for renal colic, including a prompt to remind clinicians of the CW recommendation. The impact of these changes was measured over 15 months. Results: Over the initial audit period, 17/63 (27%) of our target population received a CT to rule out renal colic. Many patients received multiple CT scans for renal colic during past ED visits, while one received a total of 13 CTs. At the time of our interventions, the baseline rate of CT scans in our target population was 37%, which reduced to 29% after our project began. Conclusion: CT is often used as an initial diagnostic modality for suspected recurrent renal colic despite current guidelines. While this initiative caused only a modest change in management, it led to the introduction of a new low-dose CT scan order specifically to reduce radiation exposure in patients at risk for repeat scans.
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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 | 0.029 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".