Attitudes Towards an Evidence-Based Clinical Decision Support Tool to Reduce Exposure to Ionizing Radiation
Bibliographic record
Abstract
Patients who suffer minor brain injuries experience unnecessary ionizing radiation in the form of a non-contrast head CT scan despite the dearth of evidence supporting standard CT scans for all brain injuries. Exposure to ionizing radiation increases the incidence of certain types of cancer. This evidence-based practice change project assesses the attitude of clinicians towards evidence-based clinical decision support tools, specifically the Canadian CT head rule. The use of highly sensitive clinical decision support tools is supported in the literature to help healthcare providers mitigate the risk associated with unnecessary use of CT scan imaging studies. The project was conducted in an academic medical center in the Northeast, utilizing healthcare providers caring for adult patients admitted to the hospital who sustained a minor brain injury due to a fall during their inpatient stay. The standard practice at this institution was to evaluate patients with minor brain injuries with non-contrast head CT scan. The Evidence-Based Practice Attitude Scale was utilized in conjunction with one-on-one instruction regarding the Canadian CT Head Rule. Participants were asked to complete a pre-test comprised of four clinical scenarios regarding patients with minor brain injuries according to what they believed to be standard practice. Subsequently, they were asked to complete the same clinical scenario questions by applying the clinical decision tool. Analysis utilized descriptive statistics, correlations of attitude domains, and knowledge increase. The healthcare provider’s attitude towards innovation is an antecedent toward the likelihood of adopting evidence-based practices guidelines into clinical practice, and there was an increase in knowledge regarding the use of clinical decision support tools.
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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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".