World federation of interventional and therapeutic neuroradiology (WFITN) federation assembly neurointerventional surgery safety checklist
Bibliographic record
Abstract
Over the last 10 years, there has been a rise in neurointerventional case complexity, device variety and physician distractions. Even among experienced physicians, this trend challenges our memory and concentration, making it more difficult to remember safety principles and their implications. Checklists are regarded by some as a redundant exercise that wastes time, or as an attack on physician autonomy. However, given the increasing case and disease complexity along with the number of distractions, it is even more important now to have a compelling reminder of safety principles that preserve habits that are susceptible to being overlooked because they seem mundane. Most hospitals have mandated a pre-procedure neurointerventional time-out checklist, but often it ends up being done in a cursory fashion for the primary purpose of 'checking off boxes'. There may be value in iterating the checklist to further emphasize safety and communication. The Federation Assembly of the World Federation of Interventional and Therapeutic Neuroradiology (WFITN) decided to construct a checklist for neurointerventional cases based on a review of the literature and insights from an expert panel.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".