Sensitivity study for CT image use in Monte Carlo treatment planning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An important step in Monte Carlo treatment planning (MCTP), which is commonly performed uncritically, is segmentation of the patient CT data into a voxel phantom for dose calculation. In addition to assigning mass densities to voxels, as is done in conventional TP, this entails assigning media. Mis-assignment of media can potentially lead to significant dose errors in MCTP. In this work, a test phantom with exact-known composition was used to study CT segmentation errors and to quantify subsequent MCTP inaccuracies. For our test cases, we observed dose errors in some regions of up to 10% for 6 and 15 MV photons, more than 30% for an 18 MeV electron beam and more than 40% for 250 kVp photons. It is concluded that a careful CT calibration with a suitable phantom is essential. Generic calibrations and the use of commercial CT phantoms have to be critically assessed.
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.
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 it