Applicability of ‘Toolkit for Safety Assessment’ tool to interventional radiology using probabilistic risk assessment techniques
Bibliographic record
Abstract
Interventional radiology brings extensive benefits to patients. Nevertheless, certain procedures may result in high doses of radiation, leading to health risks to occupationally exposed individuals (OEIs). Therefore, a more comprehensive risk analysis is essential to ensuring safety and minimising radiation exposures for all OEIs. The Toolkit for Safety Assessment (TOKSA) tool performs risk assessments based on the concepts described in 'General Safety Requirements' Part 3 (Radiation Protection and Safety of Radiation Sources: International Basic Safety Standards) and Part 4 (Safety Assessment for Facilities and Activities). This tool was developed based on the 'Ibero-American Forum of Radiological and Nuclear Regulatory Agencies' risk models and can promote the use of the risk assessment processes by OEIs. The aim of this study was to experimentally analyse the applicability of the TOKSA tool in interventional radiology with the use/support of probabilistic risk assessment techniques. The results were used to reduce the risks associated with a hemodynamics room in a hospital in Belo Horizonte, Brazil.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".