Technical Note: Removing the stem effect when performing Ir‐192 HDR brachytherapy <i>in vivo</i> dosimetry using plastic scintillation detectors: A relevant and necessary step
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate whether or not a stem effect removal technique is necessary when performing Ir-192 HDR brachytherapy in vivo dosimetry using a scintillation detector. METHODS: A red-green-blue photodiode connected to a multichannel electrometer was used to detect the light emitted from a plastic scintillation detector (PSD) during irradiation with an Ir-192 HDR brachytherapy source. Accuracy in dose measurement was compared with and without the use of stem effect removal techniques. Monochromatic and polychromatic filtration techniques were studied. An in-house template was built for accurate positioning of catheters in which the source and the PSD were inserted. Dose distribution was measured up to 5 cm from source to detector in the radial and longitudinal directions. RESULTS: The authors found the stem effect to be particularly important when the source was close to the optical fiber guide and far from the scintillation component of the detector. It can account for up to (72 +/- 3)% of the signal under clinically relevant conditions. The polychromatic filtration outperformed the monochromatic filtration as well as the absence of filtration in regard to dose measurement accuracy. CONCLUSIONS: It is necessary to implement a stem effect removal technique when building a PSD for in vivo dosimetry during Ir-192 HDR brachytherapy. The PSD that the authors have developed for this study would be suitable for such an application.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".