<i>In vivo</i>dosimetry: trends and prospects for brachytherapy
Bibliographic record
Abstract
The error types during brachytherapy (BT) treatments and their occurrence rates are not well known. The limited knowledge is partly attributed to the lack of independent verification systems of the treatment progression in the clinical workflow routine. Within the field of in vivo dosimetry (IVD), it is established that real-time IVD can provide efficient error detection and treatment verification. However, it is also recognized that widespread implementations are hampered by the lack of available high-accuracy IVD systems that are straightforward for the clinical staff to use. This article highlights the capabilities of the state-of-the-art IVD technology in the context of error detection and quality assurance (QA) and discusses related prospects of the latest developments within the field. The article emphasizes the main challenges responsible for the limited practice of IVD and provides descriptions on how they can be overcome. Finally, the article suggests a framework for collaborations between BT clinics that implemented IVD on a routine basis and postulates that such collaborations could improve BT QA measures and the knowledge about BT error types and their occurrence rates.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".