TH‐C‐VaIA‐04: Accurate Dosimetry in Photon Build‐Up Region
Bibliographic record
Abstract
Accurate measurements in the dose build‐up region for high energy photon beams are not easily obtained. While dosimetry in situations where electronic equilibrium exists is well understood, there is in general no consensus on the most accurate method for measuring doses in the build‐up region. In the past, data acquired with an extrapolation chamber were regarded as the benchmark by which the data from other more commonly used dosimeters are evaluated. As extrapolation chambers are clinically impractical devices there is a need to study the behaviour of commercially available and clinically suitable detector systems for accurate dosimetry in the build‐up region. This lecture will provide an overview on the characteristics of the dose build‐up region measurements for photon beams, the suitability of several clinical dosimeters for build‐up dose measurement and limitations in measurement accuracy. Educational Objectives: 1. Understanding the reasons why doe measurements in the build‐up region are challenging. 2. Grasp issues of the suitability of several dosimeters for measurements in the dose build‐up region. 3. Discussion of ongoing research on dosimetry in the dose build‐up region. 4. Discussion of the associated uncertainties and their clinical relevance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".