Poster - Thur Eve - 63: Prostate IMRT: <b> <i>Product-Mixture</i> </b> model of a two-dimensional probability density function integrating the variability of the motion of the rectum and the rectal wall thickness
Bibliographic record
Abstract
This study investigated the dependence of the probability density function (pdf) describing rectal wall geometry on the rectum position (Rm) and the rectal wall thickness (tw). Probability density functions describing the organ motion uncertainties of the rectum (pdfM) have been reported by many authors. In this study, we further proposed a pdf describing the changes in rectal wall thickness (pdfTW) and hence introduced a two-dimensional function pdfM&TW, incorporating the variability of RM and tW using their pdfM and pdfTW, respectively. Our study is based on the average, , of 587 prostate patients. The new pdfTW was established as a mixture of a three-mode distribution with specific mean value (μ), standard deviation (σ) and weight (w), namely, (μF = 3.31, σF = 1.82 and wF = 77.1%), (μPF = 7.7, σPF = 0.809 and wPF = 15.2%) and (μE = 10.27, σ = 0.906 and wE = 9.4%) for the full, partially full and empty state of the rectum, respectively. The pdfM&TW function was introduced as a product-mixture model of the two functions pdfM and pdfTW and it has been graphically and mathematically reviewed. Our pdfM&TW model is a more realistic representation of the probability of the geometric rectal configuration that can occur during prostate IMRT than the model using only the motional pdfM and assuming a constant rectal wall thickness in the plan optimization.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".