Poster ‐ 30: Use of a Hazard‐Risk Analysis for development of a new eye immobilization tool for treatment of choroidal melanoma
Bibliographic record
Abstract
Purpose: Our treatment for choroidal melanoma utilizes the GTC frame. The patient looks at a small LED to stabilize target position. The LED is attached to a metal arm attached to the GTC frame. A camera on the arm allows therapists to monitor patient compliance. To move to mask‐based immobilization we need a new LED/camera attachment mechanism. We used a Hazard‐Risk Analysis (HRA) to guide the design of the new tool. Method: A pre‐clinical model was built with input from therapy and machine shop personnel. It consisted of an aluminum frame placed in aluminum guide posts attached to the couch top. Further development was guided by the Department of Defense Standard Practice ‐ System Safety hazard risk analysis technique. Results: An Orfit mask was selected because it allowed access to indexes on the couch top which assist with setup reproducibility. The first HRA table was created considering mechanical failure modes of the device. Discussions with operators and manufacturers identified other failure modes and solutions. HRA directed the design towards a safe clinical device. Conclusion: A new immobilization tool has been designed using hazard‐risk analysis which resulted in an easier‐to‐use and safer tool compared to the initial design. The remaining risks are all low probability events and not dissimilar from those currently faced with the GTC setup. Given the gains in ease of use for therapists and patients as well as the lower costs for the hospital, we will implement this new tool.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".