Indications for and results of outpatient computed tomography and magnetic resonance imaging in Ontario.
Bibliographic record
Abstract
OBJECTIVE: Population rates of computed tomography (CT) and magnetic resonance imaging (MRI) continue to increase markedly. However, little is known about the indications for and results of these imaging tests. METHODS: A cross-sectional chart-abstraction study was used to determine the indications for and results of outpatient CT and MRI scans performed on or after January 1, 2005, at randomly selected Ontario hospitals. RESULTS: We studied 11,824 CT and 11,867 MRI scans. Cancer-related indications accounted for over 50% of CT scans of the abdomen-pelvis and chest. Headache was the most frequent indication for CT of the brain. More than one-half of MRI scans of the extremities were for knee pain or suspected meniscal tear. Back pain and radiculopathy were the most frequent indications for MRI of the spine. There was considerable variation between institutions in ordering patterns, with as much as a 70-fold difference between hospitals in the frequency of scans ordered for a specific indication. Less than 2% of CT scans of the brain for headache found abnormalities that could explain the headache, while over 90% of MRI scans of the spine for back pain were abnormal, although the clinical importance of the abnormalities was unclear. CONCLUSIONS: These data are a starting point for a discussion about appropriateness. Further information will be obtained by examining individual indications more closely, and linking these data to administrative databases to evaluate the impact of these imaging tests on clinical practice.
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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 | 0.000 | 0.000 |
| 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".