An implantable radiation dosimeter for use in external beam radiation therapy
Bibliographic record
Abstract
An implantable radiation dosimeter for use with external beam therapy has been developed and tested both in vitro and in canines. The device uses a MOSFET dosimeter and is polled telemetrically every day during the course of therapy. The device is designed for permanent implantation and also acts as a radiographic fiducial marker. Ten dogs (companion animals) that presented with spontaneous, malignant tumors were enrolled in the study and received an implant in the tumor CTV. Three dogs received an additional implant in collateral normal tissue. Radiation therapy plans were created for the animals and they were treated with roughly 300 cGy daily fractions until completion of the prescribed cumulative dose. The primary endpoints of the study were to record any adverse events due to sensor placement and to monitor any movement away from the point of placement. No adverse events were recorded. Unacceptable device migration was experienced in two subjects and a retention mechanism was developed to prevent movement in the future. Daily dose readings were successfully acquired in all subjects. A rigorous in vitro calibration methodology has been developed to ensure that the implanted devices maintain an accuracy of +/-3.5% relative to an ionization chamber standard. The authors believe that an implantable radiation dosimeter is a practical and powerful tool that fosters individualized patient QA on a daily basis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".