Poster — Thur Eve — 38: Review of couch parameters using an FMEA
Bibliographic record
Abstract
To improve patient safety during positioning, we undertook a systematic review of the processes used by our center to obtain couch positions. We used a Failure Mode and Effects Analysis (FMEA) framework and fifteen different possible failures were identified and rated. The three major failures were 1) Loss of planned couch position and bias from the previous day's couch position, 2) DICOM origin or isocenter is different between two plans (imaging or treatment), and 3) Patient shift in opposite direction than intended. The main effect of these failures was to cause an override of couch parameters. Based on these results, we modified our processes, introduced new QA and software checks and developed new tolerance tables so as to improve system robustness and increase our success rate at catching failures before they can affect the patient. It has been a year since we made these modifications. Based on our results, we have reduced the number of overrides at our center from a maximum of 20.5% to a maximum of 6.3%, with an average at 4% of daily treatments. Our results suggest that FMEA is an effective tool in improving treatment quality that could be used in other centers.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".