LDR to HDR: RADPOS applications in brachytherapy
Bibliographic record
Abstract
The RADPOS in vivo dosimetry system combines an electromagnetic positioning sensor and either one or five MOSFET dosimeters. The feasibility of using the system for quality control has been explored for a range of radiotherapy treatment techniques including most recently transperineal interstitial permanent prostate brachytherapy and high dose rate (HDR) treatments. Dose and position information was collected by a RADPOS array detector inside a Foley catheter within patients' urethra during permanent seed implantation. Ten patients were studied, and average displacement during implantation was Δr = (1.4-5.1) mm, with movements up to 9.7 mm due to the removal of the transrectal ultrasound probe. Maximum integral dose in the prostatic urethra ranged from 110-195 Gy, and it was found that the dose can change up to 63 cGy (62.0%) depending on whether the rectal probe is in place. For HDR, a RADPOS detector was first calibrated with an Ir-192 source. A treatment was then simulated using a total of 50 dwell positions in 5 catheters in an acrylic phantom. Dwell positions ranged from 1 to 10 cm away from the RADPOS detector and dose was measured for each source position. An average calibration coefficient of 0.74±0.11 cGy/mV was calculated for the detector and the average absolute difference between measured values and expected dose was 0.7±5.4 cGy (5±20%). The demonstrated accuracy of RADPOS dose measurements along with its ability to simultaneously measure displacement makes it a powerful tool for brachytherapy treatments, where high dose gradients can present unique in vivo dosimetry challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".