Impact and Culture Change After the Implementation of a Preprocedural Checklist in an Interventional Radiology Department
Bibliographic record
Abstract
OBJECTIVE: It has been accepted that the implementation of the a preprocedural surgical checklist can reduce perioperative morbidity and mortality in the operating suite. From this success, there has been focus on applying this intervention to other clinical areas. The objective of this study was to evaluate the acceptance and culture change after the implementation of a preprocedural checklist in the interventional radiology suite. METHODS: A preimplementation audit was performed to identify the need for a checklist in the department. A checklist was then developed, based on the surgical model. At 1 and 12 months after implementation, a survey was distributed to the staff at 3 separate teaching centers. RESULTS: Results showed that opinion of the checklist was generally positive, with staff agreement that it served as an important communication tool was in the patient's best interest, and presented a good opportunity for the team to identify important issues. CONCLUSIONS: The checklist was regarded as having little effect on delay between cases. In our setting, the checklist has become a useful and consistent safety measure to ensure that relevant patient data are brought to the forefront before intervention. As a secondary benefit, it also serves as an important communication tool and improves collaboration among team members.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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, 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".