Combined lumbar spine MRI and CT appropriateness checklist: a quality improvement project in Saskatchewan, Canada
Bibliographic record
Abstract
BACKGROUND: As rates of advanced imaging for lower back pain (LBP) continue to increase, there is a need to ensure the appropriateness of imaging. OBJECTIVE: The goal of this project was to reduce the number of inappropriate magnetic resonance imaging (MRI) and computed tomography (CT) requests for LBP patients and facilitate appropriate imaging by developing a combined imaging appropriateness checklist for lumbar spine MRI and CT. METHODS: In prior work, we developed and adopted individual evidence-based lumbar spine MRI and CT checklists into the radiology requisition process. In the current project, a combined checklist was developed and trialed in one of the former Saskatchewan health regions (Five Hills) beginning in May 2018. Using statistical process control, control charts compared the monthly number of imaging requests pre-checklist implementation and post-checklist implementation from May 2017 to February 2020. The monthly number of lumbar spine MRI and CT requisitions in the nearby former Saskatchewan Regina Qu'Appelle Health Region, in which the combined checklist was not trialed, was also plotted and compared as a balancing measure. RESULTS: In Five Hills, a shift (decrease) was observed in the monthly number of lumbar spine MRI requisitions 7 months following the implementation of the combined checklist. However, the monthly number of lumbar spine CT requisitions did not change significantly. In the Regina Qu'Appelle Health Region, there was a shift (increase) in the monthly number of lumbar spine MRI requisitions, while the monthly number of lumbar spine CT requests decreased after the implementation of the combined checklist. CONCLUSIONS: The combined checklist with evidence-based indications for lumbar spine MRI and CT imaging in LBP patients appeared to reduce the complexity associated with two previous individual checklists and facilitate imaging appropriateness. Accountable benefits may include the reduction of radiation exposure as a result of unnecessary and repeated imaging and reduction in wait times for CT and/or MRI.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".