Which accelerator photon beams are “clinic‐like” for reference dosimetry purposes?
Bibliographic record
Abstract
Previous work has demonstrated that, for photon beam dosimetry, TPR10(20) is not an ideal beam quality specifier for all bremsstrahlung beams, especially for lightly filtered beams in some standards laboratories. This paper addresses the following questions: Is TPR10(20) an adequate beam quality specifier for all modern clinical therapy accelerators? When can nonclinical beams in standards laboratories be used to calibrate ion chambers or measure kQ factors as a function of TPR10(20)? Based on detailed Monte Carlo simulations of Varian, Siemens, Elekta, and GE (Saturn) accelerators one can conclude that TPR10(20) is an adequate beam quality specifier for all these machines in the sense that for a given value of TPR10(20), the value of stopping-power ratios is the same. It is shown that, for the heavily filtered beams used in standards laboratories, TPR10(20) is an adequate beam quality specifier. It is also demonstrated that, for a larger range of bremsstrahlung beams than previously, %dd(10)x is a good beam quality specifier for all clinical beams as well as the lightly and heavily filtered beams in some standards laboratories. A criterion, based on the measured values of TPR10(20) and %dd(10)x for the beam, is proposed for determining whether a nonclinical beam is well specified by TPR10(20). Agreement between calculations for specific accelerators and measured beam quality specifiers is shown to be good, but agreement with published data for a variety of clinical accelerators is not as good. Possible reasons for the discrepancy are discussed.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".