Poster — Thur Eve — 46: The upcoming international code of practice for small static photon field dosimetry
Bibliographic record
Abstract
The increased use of small photon fields in stereotactic and intensity-modulated radiotherapy has raised the need for standardizing the dosimetry of such fields using procedures consistent with those for conventional radiotherapy. An international working group, established by the IAEA in collaboration with AAPM and IPEM, is finalising a Code of Practice for the dosimetry of small static photon fields. Procedures for reference dosimetry in nonstandard machine specific reference (msr) fields are provided following the formalism of Alfonso et al. (Med. Phys. 35: 5179; 2008). Reference dosimetry using ionization chambers in machines that cannot establish a conventional 10 cm × 10 cm reference field is based on either a direct calibration in the msr field traceable to primary standards, a calibration in a reference field and a generic correction factor or the product of a correction factor for a virtual reference field and a correction factor for the difference between the msr and virtual fields. For the latter method, procedures are provided for determining the beam quality in non-reference conditions. For the measurement of field output factors in small fields, procedures for connecting large field measurements using ionization chambers to small field measurements using high-resolution detectors such as diodes, diamond, liquid ion chambers, organic scintillators and radiochromic film are given. The Code of Practice also presents consensus data on correction factors for use in conjunction with measured, detector-specific output factors. Further research to determine missing data according to the proposed framework will be strongly encouraged by publication of this document.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.057 |
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".