Poster — Thur Eve — 23: Statistical analysis and verification of the percentage depth dose calculation based on the tissue maximum ratio in external beam radiotherapy
Bibliographic record
Abstract
The aim of this study is to perform a statistical analysis to verify the calculated percentage depth dose (PDD) based on the tissue maximum ratio (TMR) with the PDD measurements taken in water. 6 and 15 MV photon beams produced by a Varian linear 2100 C/D accelerator were used. PDDs and TMRs were measured at various depths and field sizes (5 × 5, 10 × 10, 15 × 15, 20 × 20 and 30 × 30 cm2) using a PTW 31006 ionization chamber and a scanning water tank. By comparing the calculated and measured PDD results, it was seen that for larger field sizes the deviation between the calculated and measured PDD was smaller. Deviations between the calculated and measured results were found to be higher in the build‐up regions of the 6 and 15 MV photon beams. For the statistical analyses, t‐tests were performed using the measured and calculated PDDs for each field size but showed insignificant deviations for the 6 and 15 MV photon beams. The mean t‐test values are 0.952 and 0.970 for the 6 and 15 MV photon beams, respectively. The difference between the calculated and measured PDD is within the acceptable range according to the ICRU reports (ICRU Report No. 24, 1976). We conclude that accurate calculation of the PDD using the measured TMR data is possible, which is useful as the PDD cannot be measured directly.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".