<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.
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How this classification was reachedexpand
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".