Poster — Thur Eve — 18: Differential Dose‐Volume Histogram Modeling Using the Gaussian Error Function
Bibliographic record
Abstract
The Gaussian error function (GEF) was first used to model rectal differential dose‐volume histograms (dDVH) for prostate intensity modulated radiation therapy (IMRT) plans incorporated with the interfraction prostate motion. Seven‐beam IMRT treatment plans were created in three patients with small (40 cm3), medium (53 cm3) and large (87 cm3) prostate volume, selected from a group of 20 patients. The interfraction prostate motions were measured by comparing the digitally‐reconstructed radiographs (anterior and lateral views) from the original treatment plans to the corresponding daily electronic portal images in the treatment unit based on the implanted fiducial gold markers. The ranges of prostate motion were found to be 8 – 2 mm, 4 – 8 mm and 4 – 3 mm along the anterior‐posterior directions for the small, medium and large prostate patient, respectively. Rectal dDVH varying with the interfraction prostate motion were determined by the treatment planning system (TPS), and modeled by the GEF for the three patients. It was found that the rectal dDVH from the prostate plans modeled by the GEF agreed well with those calculated by the TPS. The successful modeling of dDVH results in a significant reduction of the dDVH database, because typically about 12 parameters of the GEF model can be used to substitute about 800 – 1000 dose‐volume bin set for each dDVH. This can greatly reduce the computer memory in the normal tissue complication probability calculation associated with a huge dDVH database.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".