In Vivo Dosimetry in Radiotherapy: Techniques, Applications, and Future Directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In vivo dosimetry (IVD) is a vital component of modern radiotherapy, ensuring accurate and safe delivery of radiation doses to patients by measuring dose parameters during treatment. This paper provides a comprehensive overview of IVD, covering its fundamental principles, historical development, and the technologies used in clinical practice. Key techniques, including thermoluminescent dosimeters (TLDs), optically stimulated luminescent dosimeters (OSLDs), diodes, metal-oxide-semiconductor field-effect transistors (MOSFETs), and electronic portal imaging devices (EPIDs), are discussed, highlighting their clinical applications, advantages, and limitations. The role of IVD in external beam radiotherapy, brachytherapy, and pediatric treatments is emphasized, particularly its contributions to quality assurance, treatment validation, and error mitigation. Challenges such as measurement uncertainties, technical constraints, and integration into clinical workflows are explored, along with potential solutions and emerging innovations. The paper also addresses future perspectives, including advancements in artificial intelligence, adaptive radiotherapy, and personalized dosimetry systems. This entry underscores the critical role of IVD in enhancing the precision and reliability of radiotherapy, advocating for ongoing research and technological development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it