Appropriateness of MRI Requests for Low Back Pain and Neck Pain
Bibliographic record
Abstract
BACKGROUND: There is a high prevalence of low back pain and neck pain in Canada, and a large proportion can be treated without spine magnetic resonance imaging (MRI). We hypothesized that there is overuse of lumbar and cervical spine MRI. The primary objective was to describe the proportion of appropriate, possibly appropriate, and inappropriate MRI requests for low back pain and neck pain. METHODS: We conducted a retrospective observational study in the electromyography (EMG) clinic in Centre Hospitalier Universitaire de Sherbrooke. All ambulatory cases of low back pain or neck pain who had an EMG evaluation and a request of lumbar and/or cervical spine MRI between March 1, 2018, and May 31, 2018, were analyzed. One hundred and twenty MRI orders were classified as appropriate, possibly appropriate, and inappropriate according to the interactive decision support guide of Institut National d'Excellence en Santé et Services Sociaux for optimal use of MRI. RESULTS: Sixty-three requests (53%) were classified as inappropriate, with a higher proportion in the cervical group (34 (64%)) than the lumbar group (28 (43%)). Appropriate and possibly appropriate requests were 19 (16%) and 38 (31%), respectively. The subgroup with an MRI ordered within 90 days of symptom onset had a similar proportion of inappropriate use. INTERPRETATION: Our study demonstrates that despite recommendations against ordering spine MRI in low back pain or neck pain without red flags, there is an overuse of this imaging modality in our region, contributing to the delay in MRI access for appropriate indications.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".