Characterization of an amorphous silicon electronic portal imaging device for use as a relative dosimeter in radiation therapy
Bibliographic record
Abstract
Cancer is the second leading cause of death in Canada and in the United States. Over 50% of cancer patients will receive radiation therapy as a part of or all of their treatment. In order to ensure proper tumour coverage and normal tissue avoidance, accurate dose verification is vital. Increased use of advanced techniques such as Intensity Modulated Radiation Therapy (IMRT) has accelerated the need for real-time digital dose verification of patient fields. Dosimetric properties of the EPID were studied and results were compared to methods of dosimetry currently used at Princess Margaret Hospital (PMH). The EPID displayed a linear response to dose, good short-term reproducibility and was not significantly affected by changes in dose rate. Comparisons of the EPID with current relative dosimetry methods have shown that its dose profiles closely match those of film and water phantom for static, dynamic wedge-shaped, and IMRT fields. There is an energy response with the EPID, which is due to the high atomic number content of its detector material. The long-term stability trend of the EPID is somewhat uncertain and further investigation is recommended. Overall, the amorphous silicon electronic portal imaging device (EPID) not only shows promise as relative dosimeter, but also as an efficient tool for IMRT quality assurance.
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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 | 0.001 | 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".