Interstitial HDR breast
Bibliographic record
Abstract
Task Group 186 provides guidance for commissioning model-based dose calculation algorithms (MBDCAs) in brachytherapy treatment planning systems. TG-186 recommends a two-level commissioning approach, including comparison of absolute dose and 3D dose distributions against reference calculations. This dataset corresponds to a computational patient phantom model generated from a clinical multi-catheter 192Ir HDR breast brachytherapy case. Note: This dataset was originally hosted on the IROC platform, as described in the user guide. The files uploaded to Zenodo (version 1) are identical to those previously available from IROC and were transferred without modification to ensure full traceability and continuity with the original reference dataset. The only exception is the Elekta user guide, which was not available in the original repository. Refer to the publication for additional details: Vasiliki Peppa, Rowan M. Thomson, Shirin A. Enger, Gabriel P. Fonseca, Choonik Lee, Joseph N. E. Lucero, Firas Mourtada, Frank-André Siebert, Javier Vijande, Panagiotis Papagiannis. A MC-based anthropomorphic test case for commissioning model-based dose calculation in interstitial breast 192-Ir HDR brachytherapy. Med Phys. 50, 4675-4687 (2015) https://doi.org/10.1002/mp.16455
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.060 |
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".