Determination of percentage depth-dose curves for electron beams using different types of detectors
Bibliographic record
Abstract
According to the new AAPM TG-51 dosimetry protocol, reference dosimetry for electron beams is performed at depth of d(ref)=0.6R50-0.1 (cm) instead of d(max) recommended in TG-21. In clinical practice most electron beams are normalized at d(max). Therefore it becomes more important to get an accurate percentage-depth-dose (%dd) curve particularly for higher-energy electron beams in which the depth d(ref) is away from d(max). When ionization chambers are used in determining %dd curves the water-to-air stopping-power ratios and the fluence correction factors are required. The TG-51 recommends that the stopping-power ratios for realistic electron beams be used instead of the monoenergetic stopping-power ratios used in TG-21. This investigation aims to study the effects of those correction factors on the determination of %dd curves. We observed 1% deviations in the value of %dd at d(ref) for 15 and 18 MeV beams between a plane-parallel NACP and a cylindrical IC-10 chamber without considering the fluence correction factors P(fl). We explored a method to derive the fluence correction factors at any depth by using the existing fluence correction data at d(max) and tested its feasibility. We compared %dd curves measured by a diode detector and a NACP chamber with stopping-power ratios recommended by TG-51 and those recommended by TG-21. We found that for 15 and 18 MeV beams the difference in the values of %dd at d(ref) between using those two different stopping-power ratios is about 0.5%. Excellent agreement is found between %dd curves measured by the diode and by the NACP chamber when the stopping-power ratios recommended by TG-51 are used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".