Sci‐AM2 Sat ‐ 09: Towards objective plan comparisons in radiation therapy
Bibliographic record
Abstract
In recent years, various novel techniques for radiation treatment of cancer patients have been introduced to clinical practice: intensity‐modulated radiation therapy (IMRT), intensity‐modulated arc therapy (IMAT), helical tomotherapy (HT), light ions irradiation, etc. Such variety of instrumentation possibilities requires some initial comparative assessment of which particular technique would be the most beneficial for a given patient case. In clinical practice a balanced trade‐off between homogeneous and sufficient tumour irradiation and maximal sparing of sensitive structures is needed. A dose quality factor (DQF) was introduced to evaluate the plan quality for different treatment techniques based on realistic clinical requirements for target and organs at risk irradiation. A correlation between plan quality quantified by DQF with some set of patient specific features characterised by patient feature factor (PFF) is analysed in a comparative planning studies for 15 patients with stage III inoperable non‐small cell lung cancer using 3D conformal technique, IMRT and HT. For this set of patients, PFF is chosen as the product of three patient characteristics: the target complexity parameter, overlap between target and lungs, and the ratio between involved and non‐involved lungs. Future work would require validation in larger patient data sets, other disease sites and weighting of different factors of the PFF. This approach can help to select the most beneficial treatment technique prior to actual planning.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".