Poster — Thur Eve — 45: Commissioning of the Varian ECLIPSE eMC algorithm for clinical electron treatment planning
Bibliographic record
Abstract
Fast electron Monte Carlo systems have been developed commercially, and implemented for clinical practice in radiation therapy clinics. In this work the Varian eMC (electron Monte Carlo) algorithm was commissioned for clinical electron beams of energies between 6 MeV and 20 MeV. Beam outputs, PDDs and profiles were measured for 29 regular and irregular cutouts using the IC‐10 (Wellhöfer) ionization chamber. Detailed percentage depth dose comparisons showed that the agreement between measurement and eMC for different characteristic points on the PDD are generally less than 1 mm and always less than 2 mm, with the eMC calculated values being lower than the measured values. Of the 145 measured output factors, 19 cases fail a ±2% agreement but only 8 cases fail a ±3% agreement between calculation and measurement. Comparison of central axis dose distributions for two electron energies (9, and 20 MeV) for a 10 × 10 cm2 field, centrally shielded with Pb of width 0 cm (open), 1, 2 and 3 cm, shows agreement to within 3% except near the surface. Comparison of central axis dose distributions for 9 MeV in heterogeneous phantoms including bone and lung inserts showed agreement of 1 mm and 3 mm respectively with measured TLD data. The overall agreement between measurement and eMC calculation has enabled us to begin implementing this calculation model for clinical use.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".