A Patient-focused Information Design Intervention to Support the Minor Traumatic Brain Injuries (mTBI) Choosing Wisely Canada Recommendation
Bibliographic record
Abstract
Introduction The first Choosing Wisely Canada (CWC) recommendation for Emergency Medicine states: "Don't order CT head scans in adults and children who have suffered minor head injuries (unless positive for a validated head injury clinical decision rule)". In order to provide patients with information on the risks and benefits of computed tomography (CT) scans in minor traumatic brain injuries (mTBI) and to encourage discussions between patients and their doctor, we designed a patient-focused mTBI infographic for the emergency department (ED). Methods Stakeholders worked with content experts to co-design the infographic, which was posted in two emergency department (ED) waiting rooms. A survey was administered to evaluate whether the infographic influenced patient beliefs about the risks and benefits of CT scans and to gauge patient willingness to have a discussion with their doctor about the necessity of a scan. Results One hundred fifteen patients completed the survey. Prior to participating, 38% of patients thought a CT after an mTBI was always a good idea and 60% thought it was sometimes a good idea. After viewing the poster, 87% of respondents stated they better understood when a CT scan may be appropriate, 93% felt they better understood the risks of CT scans, and 76% understood that their doctor can often rule out serious illness without a CT scan. Only 19% of patients still felt that a CT was always necessary after an mTBI. Conclusions The mTBI infographic changed patient perceptions regarding the need for CT scans and increased awareness of the indications and risks of CT scans. This study demonstrates that targeted patient education materials can help support CWC recommendations.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".