The Incidence and Risk Factors for Emergency Department Imaging in Acute Renal Colic- A Population-Based Cohort Study
Bibliographic record
Abstract
Objective:To determine the incidence of and risk factors for imaging in patients presenting to the emergency department (ED) with renal colic. Subject/Patients and Methods:We conducted a population-based cohort study in the province of Ontario, utilizing linked administrative health data. Patients who presented to an ED with renal colic between April 1, 2010, and June 30, 2020, were included. The rate of initial imaging (CT scans and ultrasound [U/S]) and repeat imaging within 30 days was determined. Generalized linear models were utilized to evaluate patient and institutional-level characteristics associated with imaging, and specifically CT vs U/S. Results:There were 397,491 index renal colic events, of which 67% underwent imaging (CT 68%, U/S 27%, and CT+U/S same day 5%). Repeat imaging was performed in 21% of events (U/S in 12.5%, CT in 8.4%) at a median of 10 days. Of those with an initial U/S, 28% had repeat imaging compared with 18.5% for those with an initial CT. Undergoing an initial CT was associated with being male, urban residence, later year of cohort entry, history of diabetes mellitus and inflammatory bowel disease, and presentation to nonacademic hospitals of larger size, or with a higher volume of ED visits. Conclusion:Two-thirds of renal colic patients underwent imaging, and CT was the most utilized modality. Patients undergoing an initial CT had a lower likelihood of repeat imaging within 30 days. The utilization of CT increased over time and was more common in males and those presenting to nonacademic hospitals of larger size, or with higher ED volumes. Our study highlights the patient- and institution-level factors that need to be targeted with prevention strategies to reduce the utilization of CT scans, when possible, for cost reduction and to minimize patient exposure to ionizing radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".