Determination of the intrinsic energy dependence of LiF:Mg,Ti thermoluminescent dosimeters for <sup>125</sup>I and <sup>103</sup>Pd brachytherapy sources relative to <sup>60</sup>Co
Bibliographic record
Abstract
PURPOSE: To determine the intrinsic energy dependence of LiF:Mg,Ti thermoluminescent dosimeters (TLD-100) for (125)I and (103)Pd brachytherapy sources relative to (60)Co. METHODS: LiF:Mg,Ti TLDs were irradiated with low-energy brachytherapy sources and with a (60)Co teletherapy source. The brachytherapy sources measured were the Best 2301 (125)I seed, the OncoSeed 6711 (125)I seed, and the Best 2335 (103)Pd seed. The TLD light output per measured air-kerma strength was determined for the brachytherapy source irradiations, and the TLD light output per air kerma was determined for the (60)Co irradiations. Monte Carlo (MC) simulations were used to calculate the dose-to-TLD rate per air-kerma strength for the brachytherapy source irradiations and the dose to TLD per air kerma for the (60)Co irradiations. The measured and MC-calculated results for all irradiations were used to determine the TLD intrinsic energy dependence for (125)I and (103)Pd relative to (60)Co. RESULTS: The relative TLD intrinsic energy dependences (relative to (60)Co) and associated uncertainties (k = 1) were determined to be 0.883 ± 1.3%, 0.870 ± 1.4%, and 0.871 ± 1.5% for the Best 2301 seed, OncoSeed 6711 seed, and Best 2335 seed, respectively. CONCLUSIONS: The intrinsic energy dependence of TLD-100 is dependent on photon energy, exhibiting changes of 13%-15% for (125)I and (103)Pd sources relative to (60)Co. TLD measurements of absolute dose around (125)I and (103)Pd brachytherapy sources should explicitly account for the relative TLD intrinsic energy dependence in order to improve dosimetric accuracy.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".