ICES Report: Appropriateness: The Next Frontier in the Quest for Better Access to CT and MRI
Bibliographic record
Abstract
The IssueMedical imaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), is now an essential part of modern healthcare.The tests provide non-invasive diagnosis for a wide range of conditions, and their use has undoubtedly improved health outcomes for many individuals.However, in many developed countries, marked increases in imaging use are now straining healthcare expenditures and threatening health system sustainability.For example, in Ontario, between 1993 and 2003, the number of CT scans increased by 400% and the number of MRI scans increased by 700% (Tu et al. 2005).Despite these massive increases in capacity, Canadians have remained concerned about unreasonable wait times for CT and MRI (Priest 2009, February 23).In response, governments have committed a considerable amount of resources to address the access problem.In Ontario, some progress has been made for CT.Since the launch of the provincial wait times strategy in 2004, waits for CT have been reduced from 81 to 36 days (although they still remain above the provincially set target of 28 days).However, the same cannot be said for MRI.Despite a doubling in MRI volumes between 2002 and 2006, wait times for MRI remain at 107 days -well above the 28-day target.When seeing these patterns, one obvious question is whether, in some cases, these tests are being over-prescribed.Three recently published studies by the Institute for Clinical Evaluative Sciences (ICES) have shed some light on the question of appropriateness of diagnostic imaging in Ontario.In this brief report, we highlight the findings and implications of these studies. Provincial Audit of CT and MRI Use Key FindingsThis provincial audit of scan requisitions and reports examined the reasons for ordering and results of 11,824 outpatient CT scans and 11,867 outpatient MRI scans performed on or after January 1, 2005, from a representative, randomly selected sample of 29 Ontario hospitals (You et al. 2008).The key findings from the study are as follows:
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".