Poster - Thur Eve - 01: Development of simple and fast EBT2 film calibration procedure using PDD table
Bibliographic record
Abstract
Standard calibration procedure for EBT films is laborious and time-consuming. The objective of this work was to develop a simple and fast approach of EBT2 film calibration using PDD tables. EBT2 sheet is cut into 3 stripes of 5×25.5cm2. The strips were exposed to dose of 600, 200 and 70cGy at dmax each while placed horizontally in the middle of a 30×30×30cm3 solid water phantom. Varian 21EX 6MV 10×10cm2 beam was used with the gantry rotated to 90° and SSD of 100cm to the phantom surface. After at least 24 hours, the films were digitized with flatbed scanner (Epson10000XL), according to a modified ISP scanning protocol. All images were analysed using an in-house Matlab code and ImageJ software. The net-optical densities against depths in the solid phantom were calibrated using PDD tables measured with ionization chamber for same machine. For verification, another calibration curve was generated for the same film batch following the same calibration protocol. Seven pieces of films were exposed to known doses and these doses were reconstructed using two derived calibration curves. The proposed approach was 3.6 times faster than the standard considering the number of films used in each methods, 3 stripes compared to11 pieces. The mean relative dose difference calculated for these films using the PDD calibration and the standard methods was 1.0±1.2% and 0.5±2.2% with maximum relative differences of 3.0% and 4.7% respectively. Our results show that PDD calibration approach is much easier, faster and predicts dose more reproducibly and accurately than the standard approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".