Finding the Truth in Medical Imaging: Painting the Picture of Appropriateness for Magnetic Resonance Imaging in Canada
Bibliographic record
Abstract
BACKGROUND: Questions about the appropriateness of medical imaging exams, particularly related to magnetic resonance exams, have arisen in recent years. However, the prevalence of inappropriate imaging in Canada is unclear as inappropriate exam proportion estimates are often based on studies from other countries. Hence, we sought to compare and summarize Canadian studies related to magnetic resonance imaging appropriateness. METHODS: We completed a systematic literature search identifying studies related to magnetic resonance appropriateness in Canada published between 2003 and 2013. Two researchers independently searched and evaluated the literature available. Articles that studied or discussed magnetic resonance appropriateness in Canada were selected based on titles, abstracts, and, where necessary, full article review. Articles relating solely to other modalities or countries were excluded, as were imaging appropriateness guidelines and reviews. RESULTS: Fourteen articles were included: 8 quantitative studies and 6 editorials/commentaries. The quantitative studies reported inappropriate proportions of magnetic resonance exams ranging from 2%-28.5%. Our review also revealed substantial variations among study methods and analyses. Common topics identified among editorials/commentaries included reasons for obtaining imaging in general and for selecting a specific modality, consequences of inappropriate imaging, factors contributing to demand, and suggested means of mitigating inappropriate medical imaging use. CONCLUSIONS: The available studies do not support the common claim that 30% of medical imaging exams in Canada are inappropriate. The actual proportion of inappropriate magnetic resonance exams has not yet been established conclusively in Canada. Further research, particularly on a widespread national scale, is needed to guide healthcare policies.
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 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 | 0.085 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.030 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.021 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| 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".