SU-E-T-600: In Vivo Dosimetry for Total Body and Total Marrow Irradiations with Optically Stimulated Luminescence Dosimeters
Bibliographic record
Abstract
Purpose: To evaluate the feasibility of using optically stimulated luminescence dosimeters (OSLDs) for in-vivo dosimetry of patients undergoing Total Body and Total Marrow Irradiations (TBI and TMI). Methods: TBI treatments of 12 Gy were delivered in 6 BID fractions with the patient on a moving couch under a static 10 MV beam (Synergy, Elekta). TMI treatments of 18 Gy in 9 BID fractions were planned and delivered using a 6 MV TomoTherapy unit (Accuray). To provide a uniform dose to the entire patient length, the treatment was split into 2 adjacent fields junctioned in the thigh region. Our standard clinical practice involves in vivo dosimetry with MOSFETs for each TBI fraction and TLDs for at least one fraction of the TMI treatment for dose verification. In this study we also used OSLDs. Individual calibration coefficients were obtained for the OSLDs based on irradiations in a solid water phantom to the dose of 50 cGy from Elekta Synergy 10 MV (TBI) and 6 MV (TMI) beams. Calibration coefficients were calculated based on the OSLDs readings taken 2 hrs post-irradiation. For in vivo dosimetry OSLDs were placed alongside MOSFETs for TBI patients and in approximately the same locations as the TLDs for TMI patients. OSLDs were read 2 hours post treatment and compared to the MOSFET and TLD results. Results: OSLD measured doses agreed within 5% with MOSFET and TLD results, with the exception of the junction region in the TMI patient due to very high dose gradient and difficulty of precise and reproducible detector placement. Conclusion: OSLDs are useful for in vivo dosimetry of TBI and TMI patients. The quick post-treatment readout is an advantage over TLDs, allowing the results to be obtained between BID fractions, while wireless detectors are advantageous over MOSFETs for treatments involving a moving couch.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".