Bibliographic record
Abstract
Abstract In vivo dosimetry has been shown to be a powerful quality assurance method in modern radiation therapy. The most common tool used for in vivo dosimetry is the electronic portal imaging device (EPID) which can quantitatively image the therapeutic beam fluence exiting the patient during treatment delivery. Since the last major literature review on this topic was published five years ago, the radiation oncology community has shown continued strong interest in this subject. Commercial options have become more widely available, with a related increase in validation efforts and sensitivity testing, while new applications continue to be explored. Work has been done to understand and increase the accuracy of the EPID for dosimetric applications, as well as continued efforts to provide practical, quantitative experiences from clinical implementation of in vivo dosimetry systems. This review examines the published literature related to in vivo EPID dosimetry from January 2017 to February 2022. The literature is classified into three main topical areas: (1) new or improved algorithmic developments including validation work, (2) applications of the in vivo EPID dosimetry method, and (3) error identification and error sensitivity analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".