On the impact of ICRU report 90 recommendations on k<sub><i>Q</i></sub> factors for high‐energy photon beams
Bibliographic record
Abstract
Purpose To assess the impact of the ICRU report 90 recommendations on the beam‐quality conversion factor, kQ, used for clinical reference dosimetry of megavoltage linac photon beams. Methods The absorbed dose to water and the absorbed dose to the air in ionization chambers representative of those typically used for linac photon reference dosimetry are calculated at the reference depth in a water phantom using Monte Carlo simulations. Depth‐dose calculations in water are also performed to investigate changes in beam quality specifiers. The calculations are performed in a cobalt‐60 beam and MV photon beams with nominal energy between 6 MV and 25 MV using the EGSnrc simulation toolkit. Inputs to the calculations use stopping‐power data for graphite and water from the original ICRU‐37 report and the new proposed values from the recently published ICRU‐90 report. Calculated kQ factors are compared using the two different recommendations for key dosimetry data and measured kQ factors. Results Less than about 0.1% effects from ICRU‐90 recommendations on the beam quality specifiers, the photon component of the percentage depth‐dose at 10 cm, %dd(10)x, and the tissue‐phantom ratio at 20 cm and 10 cm, TPR , are observed. Although using different recommendations for key dosimetric data impact water‐to‐air stopping‐power ratios and ion chamber perturbation corrections by up to 0.54% and 0.40%, respectively, we observe little difference (≤0.14%) in calculated kQ factors. This is contradictory to the predictions in ICRU‐90 that suggest differences up to 0.5% in high‐energy photon beams. A slightly better agreement with experimental values is obtained when using ICRU‐90 recommendations. Conclusion Users of the addendum to the TG‐51 protocol for reference dosimetry of high‐energy photon beams, which recommends Monte Carlo calculated kQ factors, can rest assured that the recommendations of ICRU report 90 on basic data have little impact on this central dosimetric parameter.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".