Bibliographic record
Abstract
Radiation therapy is rapidly moving into the direction of image guidance, functional target volumes, hypofractionation, dose escalation and 4D deliveries. In this era of joint imaging-therapy developments, accurate dosimetry techniques are often considered an issue of the past. However, clinical reality increasingly requires dealings with non-protocol compliant reference dosimetry as well as complex charged particle disequilibrium measurements and their interpretation. This presentation consists of two parts: the first part will review principles of measurement dosimetry, definitions of detectors and phantoms, reference dosimetry for conventional as well as non-protocol compliant technologies. The second part will review principles of relative dosimetry measurements, focusing on scanning and integrated measurements usually performed at time of beam commissioning. We will conclude with a discussion of dosimetry in special cases including the photon build-up region as well as narrow and dynamic fields. Educational Objectives 1. To understand the principles of clinical measurement dosimetry. 2. To get an overview of detectors and phantoms for reference and relative dosimetry. 3. To understand reference dosimetry techniques in protocol compliant and non-compliant beam arrangements. 4. To understand relative dosimetry techniques for the purpose of 3D dose distributions and to grasp areas of complication. 5. To understand integrated relative dosimetry measurements in photon and electron beams. 6. To be aware of non electronic equilibrium types of measurements in the photon build-up and for small photon fields.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".