Probabilistic Fault Tree Analysis of a Radiation Treatment System
Bibliographic record
Abstract
Inappropriate administration of radiation for cancer treatment can result in severe consequences such as premature death or appreciably impaired quality of life. There has been little study of vulnerable treatment process components and their contribution to the risk of radiation treatment (RT). In this article, we describe the application of probabilistic fault tree methods to assess the probability of radiation misadministration to patients at a large cancer treatment center. We conducted a systematic analysis of the RT process that identified four process domains: Assessment, Preparation, Treatment, and Follow-up. For the Preparation domain, we analyzed possible incident scenarios via fault trees. For each task, we also identified existing quality control measures. To populate the fault trees we used subjective probabilities from experts and compared results with incident report data. Both the fault tree and the incident report analysis revealed simulation tasks to be most prone to incidents, and the treatment prescription task to be least prone to incidents. The probability of a Preparation domain incident was estimated to be in the range of 0.1-0.7% based on incident reports, which is comparable to the mean value of 0.4% from the fault tree analysis using probabilities from the expert elicitation exercise. In conclusion, an analysis of part of the RT system using a fault tree populated with subjective probabilities from experts was useful in identifying vulnerable components of the system, and provided quantitative data for risk management.
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
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".