SU-GG-I-67: Variation of MOSFET Calibration Factor as a Function of Dosimeter Age
Bibliographic record
Abstract
Purpose: The purpose of this study was to examine the variation of the calibration factor (mV/cGy) as a function of the dosimeter age in diagnostic and therapeutic MOSFET dosimeters. Method and Materials: Two of each diagnostic and therapeutic MOSFET dosimeters (TN-1002RD and TN-502RD, Best Medical Canada) were used in the study. They were irradiated side by side next to a 0.18 cc MDH RadCal 9015 ion chamber. An AGFA X-RAD 320 Orthovoltage x-ray irradiator was used with the beam quality of HVL 7.9 mm Al equivalent to a typical CT beam at 120 kVp. The desired beam quality was achieved by adding a filter (2.5mm Al and 0.1 mm Cu) to the tube. The diagnostic MOSFETs with a high sensitivity bias setting were exposed at 120 kVp over the lifetime of MOSFET, i.e., the age of 0 to 20,000 mV and calibration factor obtained as specified by the manufacturer. Similarly, the therapeutic MOSFETs, using a standard sensitivity bias setting, were exposed at 250 kVp (commonly used in small animal dosimetry) over the lifetime of MOSFET. Results: It was observed that the diagnostic MOSFET calibration factor remained within ±4% from the initial value from 0 mV up to the age of 12,000 mV (corresponding to a dose of ∼450 cGy), and then, the calibration factor decreased on average by 19% from 12,000 mV to 18,000 mV. The therapeutic MOSFETs calibration factor decreased linearly by approximately 3% every 3,000 mV up to the age of 18,000 mV. Conclusion: The diagnostic MOSFET detectors (TN-1002RD) maintain their initial calibration up to 12,000 mV (approximately 450 cGy), and thereafter, a larger percentage uncertainty was seen; therefore, we recommend a recalibration after the age of 15,000 mV. The therapeutic MOSFET (TN-502RD) should be recalibrated every 5,000mV over the life of the MOSFET.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".