On the accuracy of techniques for obtaining the calibration coefficient NK of Ir192 HDR brachytherapy sources
Bibliographic record
Abstract
The accuracy of interpolation or averaging procedures for obtaining the calibration coefficient N(K) for 192Ir high-dose-rate brachytherapy sources has been investigated using the EGSnrc Monte Carlo simulation system. It is shown that the widely used two-point averaging procedure of Goetsch et al. [Med. Phys. 18, 462 (1991)] has some conceptual problems. Most importantly, they recommended, as did the IAEA, averaging A(wall)N(K) values whereas one should average 1/N(K) values. In practice this and other issues are shown to have little effect except for Goetsch et al.'s methods for determining A(wall) values. Their method of generalizing the A(wall) values measured in one geometry to other geometries is incorrect by up to 2%. However, these errors in A(wall) values cause systematic errors of only 0.3% in 192Ir calibration coefficients. It is shown that A(wall) values need not be included in the averaging technique at all, thereby simplifying the technique considerably. It is demonstrated that as long as ion chambers with a flat response are used and/or very heavily filtered 250 kV (or higher) beams of x rays are used in the averaging, then almost all techniques can provide adequate accuracy.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".