To Choose or Not To Choose: Evaluating the Effect of a Choosing Wisely Knowledge Translation Initiative for Imaging in Low Back Pain by Emergency Physicians
Bibliographic record
Abstract
INTRODUCTION: We aimed to quantify the baseline familiarity of emergency medicine (EM) physicians with the Choosing Wisely Canada (CWC)-EM recommendations. We then assessed whether a structured knowledge translation (KT) initiative affected awareness, knowledge, and practice patterns for imaging in low back pain. METHODS: We completed a two-center, before and after practice evaluation study. Physicians working in two Canadian emergency departments (EDs) were asked to participate in a survey before a KT initiative, and were surveyed again at a six-month follow up period post-intervention. The primary outcome of physician practice was determined by analyzing the frequency of lumbar X-ray imaging for back pain. RESULTS: A total of 37 physicians were asked to complete the pre- and post-intervention survey. Awareness of the CWC-EM recommendations increased following the intervention (63%; 95%CI: 43-79 at baseline vs. 86%; 66-96 post-intervention). Knowledge increased with 58% (39-76) of physicians responding correctly initially, and 86% (66-96) after the intervention. Despite increases in awareness and knowledge of the guidelines, the lumbar X-ray imaging rate increased from a baseline of 12% (9.9-14.5) to 16.2% (13.6-19.2; p = 0.023) following the intervention. CONCLUSION: We demonstrated some improvements in physician awareness and knowledge of the CWC-EM recommendations following our intervention. Despite these improvements, our KT intervention was associated with an increased frequency of imaging for low back pain, contrary to our expectations.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".