Checklist for Head Injury Management Evaluation Study (CHIMES): a quality improvement initiative to reduce imaging utilisation for head injuries in the emergency department
Bibliographic record
Abstract
Over 90% of patients with head trauma seen in emergency departments (EDs) are diagnosed with minor head injuries. Over-utilisation of CT scans results in unnecessary exposure to radiation and increases healthcare utilisation. Using recommendations from the Choosing Wisely Canada (CWC) campaign and quality improvement (QI) methodology, we aimed to reduce the CT scan rate for head injuries by 10% over a 6-month period.Baseline CT scan rates were determined through a 27-month retrospective cohort review. We used stakeholder engagement and provider surveys to develop our driver diagram and Plan-Do-Study-Act (PDSA) cycles, which included (1) improving provider knowledge about the CWC campaign recommendations; (2) testing, refining and implementing a modified Canadian CT Head Rule checklist; (3) developing CWC-themed head injury-specific patient handouts; and (4) feedback on CT scan group ordering rates to providers. Our primary outcome measure was the number of CT scans performed for patients with head injuries. Process measures included the number of checklists completed and ED length of stay (LOS). Our balancing measure was return ED visits within 72 hours (with or without admission).Baseline CT scan rates prior to our interventions was 46.1%. Our QI initiative resulted in a 'shift' in the Statistical Process Control chart of the weekly CT scan rates, associated with the first and second PDSA cycles, resulting in a 13.9% reduction in CT rates during the initial 3 months, and a sustained reduction of 8% at 16 months (p<0.05). Mean ED LOS for all patients with head injuries decreased by 1.5 min (p=0.74). 33% of checklists were completed. 72-hour return visits did not change significantly (p=0.68).Through provider and patient education, and the creation of a user-friendly evidence-based tool, our local QI initiative was successful in achieving long-term reduction in CT rates for patients presenting to EDs with head injuries.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".