Bibliographic record
Abstract
In the last decade IMRT and related treatment modes have become de facto, if not actual standards of care. These modes are heavily dependent on the anatomical modeling phase of treatment planning. It may in fact be argued that this is the most important step in the planning process, as the optimization and plan evaluation will be directly affected by a good or poor anatomical model. Physicists have increasingly been called upon to do much of this contouring, yet have traditionally had only a brief formal introduction to anatomy. This session will provide a brief overview and refresher on two anatomical regions that have come to be most often treated by IMRT and like modalities. Dr. Jonn Wu will discuss the contouring of prominent normal structures and target volumes in the thorax using multiple imaging modalities, and illuminate key concepts in identifying these structures. Emphasis will be placed on relevance to SBRT. Dr. I-Chow Hsu will discuss the structures in the pelvis relevant to prostatic treatments, including pelvic lymphatic chains, using CT and the Visible Human Project. LEARNING OBJECTIVES: 1. To better understand how to identify structures in the thorax 2. To better understand how to identify structures in the pelvis relevant to prostate treatments 3. To understand the impact and pitfalls of different imaging modalities in these anatomical locations.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.029 |
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".