Sci-Thur AM: Planning - 03: Extensive patient specific QA for field junction regions for craniospinal irradiation with Jagged-Junction IMRT approach without beam edge matching for field junctions
Bibliographic record
Abstract
PURPOSE: Jagged-Junction IMRT was developed for craniospinal irradiation. An extensive QA was performed for the field junction regions. METHODS AND MATERIALS: The Jagged-Junction IMRT plan employed three field sets, each with unique isocentres (Iso1,2,3). Fields from adjacent sets were overlapped and the dose was smoothly integrated inside the overlapped junction. The delivered dose in the junction regions were verified with film and ion chamber measurements on phantoms. An anthropomorphic-wax phantom was created for verifying the cranio-spinal junction. For measuring at the spinal-spinal junction, a solid water phantom was used. The influence of beam mismatching due to setup and mechanical inaccuracy was investigated by shifting all the fields from Iso1 and Iso3 superiorly and inferiorly by 3 mm and at the same time keeping all the fields from Iso2 without any shift. RESULTS: The patient-averaged difference between the measured dose with ion chamber and planned dose in the cranio-spinal junction is 0.34 % ± 0.40% and in the spinal-spinal junction this difference is 0.03% ± 0.71%. The dose profile comparison shows that measured and planned dose profiles match well to each other over a junction region. The patient-averaged dose difference discrepancy between the film measurement and the planned is 1.1% ± 1.3% at the cranio-spinal junction and -0.14% ± 1.8% at the spinal junction. CONCLUSIONS: Jagged-Junction IMRT planning provided smooth dose coverage to the target in the field junction region. The junction dose for the Jagged-Junction IMRT plan is not sensitive to the setup error during the treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".